APPARATUS AND METHOD FOR SIMULATED VIRTUAL COMPONENT DEVELOPMENT

An apparatus for, and method of, simulated virtual component testing, including a processor and a memory, the processor configured to receive a specification datum, receive an application datum, generate emulation parameters as a function of the specification datum, generate a testing framework as a function of the emulation parameters, determine an integration datum as a function of the testing framework and the application datum, output a compatibility datum as a function of the integration datum, and display a user interface.

FIELD OF THE INVENTION

The present invention generally relates to the field of circuit development and integration. In particular, the present invention is directed to engineering development support.

BACKGROUND

Testing compatibility in a system between software applications and electronic components is vital for proper integration and functioning of the system. However, there are circumstances where access to those components is limited.

SUMMARY OF THE DISCLOSURE

In an aspect an apparatus for virtual component testing includes at least a processor and a memory communicatively connected to the at least a processor, where the memory contains instructions configuring the at least a processor to receive a specification datum, receive an application datum, generate emulation parameters as a function of the specification datum, generate an testing framework as a function of the emulation parameters, determine an integration datum as a function of the testing framework and the application datum, output a compatibility datum as a function of the integration datum, and display a user interface.

In another aspect a method of virtual component testing, wherein the method includes receiving a specification datum, receiving an application datum, generating emulation parameters as a function of the specification datum, generating a testing framework as a function of the emulation parameters, determining an integration datum as a function of the testing framework and the application datum, outputting a compatibility datum as a function of the integration datum, and displaying a user interface.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to apparatus and methods for simulated virtual component development. In an embodiment, apparatus 100 may be used to create a full digital twin of a virtual component. Exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

In an embodiment, methods and apparatuses described herein may perform or implement one or more aspects of a cryptographic system. In one embodiment, a cryptographic system is a system that converts data from a first form, known as “plaintext,” which is intelligible when viewed in its intended format, into a second form, known as “ciphertext,” which is not intelligible when viewed in the same way. Ciphertext may be unintelligible in any format unless first converted back to plaintext. In one embodiment, a process of converting plaintext into ciphertext is known as “encryption.” Encryption process may involve the use of a datum, known as an “encryption key,” to alter plaintext. Cryptographic system may also convert ciphertext back into plaintext, which is a process known as “decryption.” Decryption process may involve the use of a datum, known as a “decryption key,” to return the ciphertext to its original plaintext form. In embodiments of cryptographic systems that are “symmetric,” decryption key is essentially the same as encryption key: possession of either key makes it possible to deduce the other key quickly without further secret knowledge. Encryption and decryption keys in symmetric cryptographic systems may be kept secret and shared only with persons or entities that the user of the cryptographic system wishes to be able to decrypt the ciphertext. One example of a symmetric cryptographic system is the Advanced Encryption Standard (“AES”), which arranges plaintext into matrices and then modifies the matrices through repeated permutations and arithmetic operations with an encryption key.

In embodiments of cryptographic systems that are “asymmetric,” either encryption or decryption key cannot be readily deduced without additional secret knowledge, even given the possession of a corresponding decryption or encryption key, respectively; a common example is a “public key cryptographic system,” in which possession of the encryption key does not make it practically feasible to deduce the decryption key, so that the encryption key may safely be made available to the public. An example of a public key cryptographic system is RSA, in which an encryption key involves the use of numbers that are products of large prime numbers, but a decryption key involves the use of those very large prime numbers, such that deducing the decryption key from the encryption key requires the practically infeasible task of computing the prime factors of a number which is the product of two very large prime numbers. Another example is elliptic curve cryptography, which relies on the fact that given two points P and Q on an elliptic curve over a finite field, and a definition for addition where A+B=−R, the point where a line connecting point A and point B intersects the elliptic curve, where “0,” the identity, is a point at infinity in a projective plane containing the elliptic curve, finding a number k such that adding P to itself k times results in Q is computationally impractical, given correctly selected elliptic curve, finite field, and P and Q. A further example of asymmetrical cryptography may include lattice-based cryptography, which relies on the fact that various properties of sets of integer combination of basis vectors are hard to compute, such as finding the one combination of basis vectors that results in the smallest Euclidean distance. Embodiments of cryptography, whether symmetrical or asymmetrical, may include quantum-secure cryptography, defined for the purposes of this disclosure as cryptography that remains secure against adversaries possessing quantum computers; some forms of lattice-based cryptography, for instance, may be quantum-secure.

In some embodiments, systems and methods described herein produce cryptographic hashes, also referred to by the equivalent shorthand term “hashes.” A cryptographic hash, as used herein, is a mathematical representation of a lot of data, such as files or blocks in a block chain as described in further detail below; the mathematical representation is produced by a lossy “one-way” algorithm known as a “hashing algorithm.” Hashing algorithm may be a repeatable process; that is, identical lots of data may produce identical hashes each time they are subjected to a particular hashing algorithm. Because hashing algorithm is a one-way function, it may be impossible to reconstruct a lot of data from a hash produced from the lot of data using the hashing algorithm. In the case of some hashing algorithms, reconstructing the full lot of data from the corresponding hash using a partial set of data from the full lot of data may be possible only by repeatedly guessing at the remaining data and repeating the hashing algorithm; it is thus computationally difficult if not infeasible for a single computer to produce the lot of data, as the statistical likelihood of correctly guessing the missing data may be extremely low. However, the statistical likelihood of a computer of a set of computers simultaneously attempting to guess the missing data within a useful timeframe may be higher, permitting mining protocols as described in further detail below.

In an embodiment, hashing algorithm may demonstrate an “avalanche effect,” whereby even extremely small changes to lot of data produce drastically different hashes. This may thwart attempts to avoid the computational work necessary to recreate a hash by simply inserting a fraudulent datum in data lot, enabling the use of hashing algorithms for “tamper-proofing” data such as data contained in an immutable ledger as described in further detail below. This avalanche or “cascade” effect may be evinced by various hashing processes; persons skilled in the art, upon reading the entirety of this disclosure, will be aware of various suitable hashing algorithms for purposes described herein. Verification of a hash corresponding to a lot of data may be performed by running the lot of data through a hashing algorithm used to produce the hash. Such verification may be computationally expensive, albeit feasible, potentially adding up to significant processing delays where repeated hashing, or hashing of large quantities of data, is required, for instance as described in further detail below. In one embodiment, credentials generated are verifiable credentials that are tamper-proof statements regarding a user that are cryptographically signed by a generator of the credentials. Examples of hashing programs include, without limitation, SHA256, a NIST standard; further current and past hashing algorithms include Winternitz hashing algorithms, various generations of Secure Hash Algorithm (including “SHA-1,” “SHA-2,” and “SHA-3”), “Message Digest” family hashes such as “MD4,” “MD5,” “MD6,” and “RIPEMD,” Keccak, “BLAKE” hashes and progeny (e.g., “BLAKE2,” “BLAKE-256,” “BLAKE-512,” and the like), Message Authentication Code (“MAC”)-family hash functions such as PMAC, OMAC, VMAC, HMAC, and UMAC, Poly 1305-AES, Elliptic Curve Only Hash (“ECOH”) and similar hash functions, Fast-Syndrome-based (FSB) hash functions, GOST hash functions, the Grøstl hash function, the HAS-160 hash function, the JH hash function, the RadioGatun hash function, the Skein hash function, the Streebog hash function, the SWIFFT hash function, the Tiger hash function, the Whirlpool hash function, or any hash function that satisfies, at the time of implementation, the requirements that a cryptographic hash be deterministic, infeasible to reverse-hash, infeasible to find collisions, and have the property that small changes to an original message to be hashed will change the resulting hash so extensively that the original hash and the new hash appear uncorrelated to each other. A degree of security of a hash function in practice may depend both on the hash function itself and on characteristics of the message and/or digest used in the hash function. For example, where a message is random, for a hash function that fulfills collision-resistance requirements, a brute-force or “birthday attack” may to detect collision may be on the order of O (2n/2) for n output bits; thus, it may take on the order of 2256 operations to locate a collision in a 512 bit output “Dictionary” attacks on hashes likely to have been generated from a non-random original text can have a lower computational complexity, because the space of entries they are guessing is far smaller than the space containing all random permutations of bits. However, the space of possible messages may be augmented by increasing the length or potential length of a possible message, or by implementing a protocol whereby one or more randomly selected strings or sets of data are added to the message, rendering a dictionary attack significantly less effective.

Embodiments described in this disclosure may perform secure proofs. A “secure proof,” as used in this disclosure, is a protocol whereby an output is generated that demonstrates possession of a secret, such as device-specific secret, without demonstrating the entirety of the device-specific secret; in other words, a secure proof by itself, is insufficient to reconstruct the entire device-specific secret, enabling the production of at least another secure proof using at least a device-specific secret. A secure proof may be referred to as a “proof of possession” or “proof of knowledge” of a secret. Where at least a device-specific secret is a plurality of secrets, such as a plurality of challenge-response pairs, a secure proof may include an output that reveals the entirety of one of the plurality of secrets, but not all of the plurality of secrets; for instance, secure proof may be a response contained in one challenge-response pair. In an embodiment, proof may not be secure; in other words, proof may include a one-time revelation of at least a device-specific secret, for instance as used in a single challenge-response exchange.

Secure proof may include a zero-knowledge proof, which may provide an output demonstrating possession of a secret while revealing none of the secret to a recipient of the output; zero-knowledge proof may be information-theoretically secure, meaning that an entity with infinite computing power would be unable to determine secret from output. Alternatively, zero-knowledge proof may be computationally secure, meaning that determination of secret from output is computationally infeasible, for instance to the same extent that determination of a private key from a public key in a public key cryptographic system is computationally infeasible. In some embodiments, a verifier may authenticate the generated credential via a decentralized identifier by using public/private key pairs on an immutable sequential listing to verify that the hashed credential belongs to a specific user. Zero-knowledge proof algorithms may generally include a set of two algorithms, a prover algorithm, or “P,” which is used to prove computational integrity and/or possession of a secret, and a verifier algorithm, or “V” whereby a party may check the validity of P. Zero-knowledge proof may include an interactive zero-knowledge proof, wherein a party verifying the proof must directly interact with the proving party; for instance, the verifying and proving parties may be required to be online, or connected to the same network as each other, at the same time. Interactive zero-knowledge proof may include a “proof of knowledge” proof, such as a Schnorr algorithm for proof on knowledge of a discrete logarithm. In a Schnorr algorithm, a prover commits to a randomness r, generates a message based on r, and generates a message adding r to a challenge c multiplied by a discrete logarithm that the prover is able to calculate; verification is performed by the verifier who produced c by exponentiation, thus checking the validity of the discrete logarithm. Interactive zero-knowledge proofs may alternatively or additionally include sigma protocols. In some embodiments, through zero-knowledge proof claims, users may not need to expose their private credential and/or identity information to a decentralized platform as the validity of users' real-world credential and/or identity information is attested via a privacy-preserving protocol enabled by zero-knowledge proof technology while still remaining private. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various alternative interactive zero-knowledge proofs that may be implemented consistently with this disclosure.

Alternatively, zero-knowledge proof may include a non-interactive zero-knowledge, proof, or a proof wherein neither party to the proof interacts with the other par ty to the proof; for instance, each of a party receiving the proof and a party providing the proof may receive a reference datum which the party providing the proof may modify or otherwise use to perform the proof. As a non-limiting example, zero-knowledge proof may include a succinct non-interactive arguments of knowledge (ZK-SNARKS) proof, wherein a “trusted setup” process creates proof and verification keys using secret (and subsequently discarded) information encoded using a public key cryptographic system, a prover runs a proving algorithm using the proving key and secret information available to the prover, and a verifier checks the proof using the verification key; public key cryptographic system may include RSA, elliptic curve cryptography, ElGamal, or any other suitable public key cryptographic system. Generation of trusted setup may be performed using a secure multiparty computation so that no one party has control of the totality of the secret information used in the trusted setup; as a result, if any one party generating the trusted setup is trustworthy, the secret information may be unrecoverable by malicious parties. As another non-limiting example, non-interactive zero-knowledge proof may include a Succinct Transparent Arguments of Knowledge (ZK-STARKS) zero-knowledge proof. In an embodiment, a ZK-STARKS proof includes a Merkle root of a Merkle tree representing evaluation of a secret computation at some number of points, which may be 1 billion points, plus Merkle branches representing evaluations at a set of randomly selected points of the number of points; verification may include determining that Merkle branches provided match the Merkle root, and that point verifications at those branches represent valid values, where validity is shown by demonstrating that all values belong to the same polynomial created by transforming the secret computation. In an embodiment, ZK-STARKS does not require a trusted setup.

Zero-knowledge proof may include any other suitable zero-knowledge proof. Zero-knowledge proof may include, without limitation, bulletproofs. Zero-knowledge proof may include a homomorphic public-key cryptography (hPKC)-based proof. Zero-knowledge proof may include a discrete logarithmic problem (DLP) proof. Zero-knowledge proof may include a secure multi-party computation (MPC) proof. Zero-knowledge proof may include, without limitation, an incrementally verifiable computation (IVC). Zero-knowledge proof may include an interactive oracle proof (IOP). Zero-knowledge proof may include a proof based on the probabilistically checkable proof (PCP) theorem, including a linear PCP (LPCP) proof. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various forms of zero-knowledge proofs that may be used, singly or in combination, consistently with this disclosure.

In an embodiment, secure proof is implemented using a challenge-response protocol. In an embodiment, this may function as a one-time pad implementation; for instance, a manufacturer or other trusted party may record a series of outputs (“responses”) produced by a device possessing secret information, given a series of corresponding inputs (“challenges”), and store them securely. In an embodiment, a challenge-response protocol may be combined with key generation. A single key may be used in one or more digital signatures as described in further detail below, such as signatures used to receive and/or transfer possession of crypto-currency assets; the key may be discarded for future use after a set period of time. In an embodiment, varied inputs include variations in local physical parameters, such as fluctuations in local electromagnetic fields, radiation, temperature, and the like, such that an almost limitless variety of private keys may be so generated. Secure proof may include encryption of a challenge to produce the response, indicating possession of a secret key. Encryption may be performed using a private key of a public key cryptographic system or using a private key of a symmetric cryptographic system; for instance, trusted party may verify response by decrypting an encryption of challenge or of another datum using either a symmetric or public-key cryptographic system, verifying that a stored key matches the key used for encryption as a function of at least a device-specific secret. Keys may be generated by random variation in selection of prime numbers, for instance for the purposes of a cryptographic system such as RSA that relies prime factoring difficulty. Keys may be generated by randomized selection of parameters for a seed in a cryptographic system, such as elliptic curve cryptography, which is generated from a seed. Keys may be used to generate exponents for a cryptographic system such as Diffie-Helman or ElGamal that are based on the discrete logarithm problem.

Embodiments described in this disclosure may utilize, evaluate, and/or generate digital signatures. A “digital signature,” as used herein, includes a secure proof of possession of a secret by a signing device, as performed on provided element of data, known as a “message.” A message may include an encrypted mathematical representation of a file or other set of data using the private key of a public key cryptographic system. Secure proof may include any form of secure proof as described above, including without limitation encryption using a private key of a public key cryptographic system as described above. Signature may be verified using a verification datum suitable for verification of a secure proof; for instance, where secure proof is enacted by encrypting message using a private key of a public key cryptographic system, verification may include decrypting the encrypted message using the corresponding public key and comparing the decrypted representation to a purported match that was not encrypted; if the signature protocol is well-designed and implemented correctly, this means the ability to create the digital signature is equivalent to possession of the private decryption key and/or device-specific secret. Likewise, if a message making up a mathematical representation of file is well-designed and implemented correctly, any alteration of the file may result in a mismatch with the digital signature; the mathematical representation may be produced using an alteration-sensitive, reliably reproducible algorithm, such as a hashing algorithm as described above. A mathematical representation to which the signature may be compared may be included with signature, for verification purposes; in other embodiments, the algorithm used to produce the mathematical representation may be publicly available, permitting the easy reproduction of the mathematical representation corresponding to any file.

In some embodiments, digital signatures may be combined with or incorporated in digital certificates. In one embodiment, a digital certificate is a file that conveys information and links the conveyed information to a “certificate authority” that is the issuer of a public key in a public key cryptographic system. Certificate authority in some embodiments contains data conveying the certificate authority's authorization for the recipient to perform a task. The authorization may be the authorization to access a given datum. The authorization may be the authorization to access a given process. In some embodiments, the certificate may identify the certificate authority. The digital certificate may include a digital signature.

In some embodiments, a third party such as a certificate authority (CA) is available to verify that the possessor of the private key is a particular entity; thus, if the certificate authority may be trusted, and the private key has not been stolen, the ability of an entity to produce a digital signature confirms the identity of the entity and links the file to the entity in a verifiable way. Digital signature may be incorporated in a digital certificate, which is a document authenticating the entity possessing the private key by authority of the issuing certificate authority and signed with a digital signature created with that private key and a mathematical representation of the remainder of the certificate. In other embodiments, digital signature is verified by comparing the digital signature to one known to have been created by the entity that purportedly signed the digital signature; for instance, if the public key that decrypts the known signature also decrypts the digital signature, the digital signature may be considered verified. Digital signature may also be used to verify that the file has not been altered since the formation of the digital signature.

A “credential,” as used in this disclosure, is information related to or defining an entity's authority, authorization, status, rights, access, or entitlement to privileges. In some embodiments, a user's credential may include a validation of the user's legal identity, social identity, financial identity, proof of creation, proof of interaction, proof of personhood, and the like. For instance, in a non-limiting example, the legal identity may include the user's real name, date of birth, home address, government ID such as driver's license, credit report, social security number, and the like. As used in this disclosure, a “social identity,” is any identity information derived and/or attested from a relevant social group where the user is a member of. For instance, in a non-limiting example, the user's social identity may include identities and/or membership associated with a social media platform such as Facebook®, Twitter®, membership associated with a decentralized community, and the like. In some embodiments, the user's social identity may be the user's profile name and/or pictures associated with the user profile.

Continuing to refer to FIG. 1, in embodiment, apparatus 100 be an edge computing device. An “edge computing device,” as used herein, is a computing device configured to operate within close proximity to a data source. In some embodiments, an edge computing device may be a device with limited processing power. In a non-limiting example, edge computing device may be a gateway device configured to read data generate by one or more sensors. In some embodiments, edge computing device may be battery powered. In some embodiments, edge computing device may be configured to be portable. In embodiments, edge computing device may be enclosed by a water-resistant material. In some embodiments, edge computing device may be enclosed by a water-proof material. In some embodiments, edge computing may be enclosed in a shock-resistant casing. In some embodiments, edge computing device may be enclosed in a bullet-proof casing. In embodiments, edge computing device may include a SWaP-Optimized(Size, Weight, and Power) connectors. As used herein, “SWAP-Optimized connectors” refers to a type of connector that has been designed with the principles of Size, Weight, and Power optimization. In a non-limiting example, edge computing device may be configured to be used by tactical units in a battlefield.

In some embodiments, and still referring to FIG. 1, apparatus 100 may be a reduced instruction set computer (RISC). As used in this disclosure, “a reduced instruction set computer (RISC)” is a type of computing device, particularly a microprocessor that is designed to perform a smaller number (ranges from around 50 or less to a few hundred instructions or more) of (computer) instruction at a high speed (within one clock cycle). In some cases, RISC may include a processor using few dozen of simple instructions, which may be executed within a single clock cycle, as opposed to complex instruction set computers (CISC) that have instructions that may take a plurality of clock cycles to execute. In one or more embodiments, instructions may be classified as simple instructions when instructions only contain basic operations, such as, without limitation, arithmetic (e.g., add, subtract, multiply, divide, and/or the like), logical operations (e.g., and, or, not), data movement (import/export, upload, store, and/or the like), and any other desired operations that are designed to be performed quickly and efficiently. In some cases, instructions used by RISC processor may be fixed in length, for example, 32 bits. In some cases, memory access in RISC designs may be limited to load and store instructions such as any instructions as described herein. In some cases, however, operations executed by RISC may take more than one clock cycle, for example, execution time of one or more operations may depend on memory system's speed (but RISC may be configured to keep these operations as fast as possible). In a non-limiting example, memory 108 may be accessed through LOAD i.e., to load a memory location and STORE i.e., to write to it, wherein all other instructions may operate on one or more processor registers and processor 104 may not have direct access to manipulate memory 108. In some cases, RISC processor may include fewer addressing modes than CISC processor to simply hardware components required for decoding and/or executing instructions. In a non-limiting example, instructions may include one or more register-to-register (register-based) operation in which arithmetic and logical operations listed above are performed between registers, and if data is needed from memory 108, the data may be first loaded into at least one register. In some cases, RISC may be capable of pipeline processing, wherein simple and uniform instruction sets may enable optimal pipeline processing; for instance, subsets of instruction sets may be overlapped. Additionally, RISC may operate only on a smaller number of data types (e.g., integer, float, character, Booleans, pointers, and/or the like). RISC may be configured to perform instruction sets in parallel to increase the rate at which instructions are executed. Exemplary RISC may include, without limitation, ARM, MIPS, POWERPC, SPARC, and the like.

Still referring to FIG. 1, in embodiments, processor 104 is configured to receive a specification datum 112. A “specification datum,” as used herein, is an element of data, which describes a hardware component technical specifications and hardware emulation requirements. “Hardware component technical specifications,” as used herein, are descriptions of the physical characteristics of a component. A “hardware emulation requirement,” as used herein, is a description of the functionality of one or more components to be tested. In a nonlimiting example, specification datum 112 may describe physical characteristics of components and their functionalities. Continuing with this non-limiting example, specification datum 112 may include data representing angle of attack (AoA) sensors, Outside Air Temperature (OAT) sensors, a flight control microcontroller, and a display. In a non-limiting embodiment, specification datum 112 may include a controller card. As used herein, “controller card” refers to a hardware component that may be connected to a computing device in order to provide additional or improved capabilities. In a non-limiting embodiment, specification datum 112 may include a boot operating storage solution (BOSS) card. As used herein, a “boot operating storage solution card” refers to a specialized controlled card configured to improve the boot times on a computing device. In a non-limiting embodiment, specification datum 112 may include a virtual cross path (VPX) controller card. As used herein, “VPX controller card” refers to a controller card that has met the standards and/or limitations of the VMEbus International Trade Association. Additional disclosure related controller cards, BOSS cards, and the like may be found in U.S. patent application Ser. No. 18/422,068, filed on Jan. 25, 2024, having attorney docket number 1548-002USU1 and entitled “HARDWARE APPARATUS FOR ISOLATED VIRTUAL ENVIRONMENTS,” the entirety of which is incorporated herein by reference.

With continued reference to FIG. 1, in some embodiments, specification datum 112 may include Field Programmable Gate Array (FPGA). As used herein, “FPGA” refers to a chip that may be programmed and/or reprogrammed after manufacturing. FPGA may include a series of registers and logical gates. In a non-limiting embodiment, FPGA registers may be turned on and off in order to program FPGA. This may be used to, for example, run a specific algorithm many times using hardware rather than software instructions. In a non-limiting embodiment, specification datum 112 may include any series of registers or logical gates associated with the FPGA. Additional disclosure related to controller cards, BOSS cards, and the like may be found in U.S. patent application Ser. No. 18/422,122, filed on Jan. 25, 2024, having attorney docket number 1548-004USU1 and entitled “APPARATUS FOR HETEROGENOUS PROCESSING,” the entirety of which is incorporated herein by reference.

With continued reference to FIG. 1, in some embodiments, specification datum 112 may include a dedicated software package. As used herein, a “dedicated software package” refers to components necessary to execute a software package. In a non-limiting embodiment, dedicated software package may include a library or a plurality of libraries wherein the libraries contain pre-written code that can be called by various software and/or applications such as a software module. In an embodiment, dedicated software package may include various software configurations for a software module. As used herein, “software configuration” refers to instructions and parameters that define how the software module should operate. Additional disclosure related to dedicated software packages, and the like may be found in U.S. patent application Ser. No. 18/395,210, filed on Dec. 22, 2023, having attorney docket number 1548-003USU1, and entitled “SYSTEM AND METHOD FOR A SAFETY CRITICAL OPERATING ENVIRONMENT CONTAINER ARCHITECTURE,” the entirety of which is incorporated herein by reference.

With continued reference to FIG. 1, in some embodiments, processor 104 is configured to receive an application datum 116. As used herein, an “application datum” is a data component that describes the functioning and deployment of a software application configured to operate on a hardware component. A “software application,” as used herein is a set of instructions configuring processor 104, or any set of processors, to perform an action. This setup allows for adaptable and tailored software functionalities to be applied to hardware components, enabling them to perform complex and specific operations as dictated by the needs of the system or the user. In an embodiment, specification datum 112 may have an associated application datum 116, where the application datum describes the functioning and deployment of the specification datum and any other associated software applications.

Continuing to refer to FIG. 1, in an embodiment, at least a processor 104 is configured to determine emulation parameters 120 as a function of specification datum 112. An “emulation parameters,” as used herein, are the system configuration settings required to emulate the integration of a software program with the specific hardware components. In a non-limiting parameter, emulation parameter 120 may be configured to generate a “digital twin.” As used herein, a “digital twin” refers to a simulation of a system. A digital twin may mirror attributes and dynamic behaviors of working systems or devices. Emulation parameters found within a digital twin system may be updated and changed as the specification datum operates and is updated. Digital twin may represent hardware, firmware, BUS, virtual BUS elements, software environments (such as hypervisor, container, etc.), and any software (including third-party software) that has been loaded onto processor 104. The digital twin may be capable of simulating attributes of any of the above described elements, as well as their interactions. Interactions that may be simulated by the digital twin may include simulating data paths, processor configurations, software applications, and the like. In a non-limiting embodiment, simulating a data path allows a digital twin to analyze how data moves through the system, identify bottlenecks, and predict the impact of changes or updates within the system. In another non-limiting embodiment, digital twin may model interactions between hardware and software, such as specification datum 112 and application datum 116, to understand how they affect each other. This may include an emulation of how a software application's performance changes with various processor configurations or understanding how updates to a specification datum may impact the overall system's functionality. Digital twin and emulation parameters may allow for real-time monitoring and adjustments. This may enable predictive maintenance by identifying potential issues before they occur, optimizing performance by fine-tuning configurations, and aiding in decision-making processes by providing detailed insights into how changes may affect the system. In some embodiments, determining emulation parameters 120 may include identifying an engineering methodology 124. An “engineering methodology,” as used in this disclosure, is a systematic and structured approach for designing, testing, and implementing engineering solutions. In an embodiment, engineering methodology may comprise a framework or set of procedures that may provide a structured approach to solve engineering problems. In embodiments, engineering methodology 124 may include waterfall methodology, agile methodology, lean engineering methodology, model-based engineering (MBE) methodology, test-driven development (TDD) methodology, and the like. In a non-limiting example, emulation parameters 120 may include a model-based engineering (MBE) engineering methodology 124, where the parameters for emulation include a whole system where specification datum 112 would be tested. In another non-limiting example, emulation parameters 120 may include an agile engineering methodology, where sub-components of a component included in specification datum 112 are iteratively tested before the whole component is tested. A person with ordinary skill in the art would appreciate how each type of engineering methodology 124 may affect how a component is emulated and tested.

With continued reference to FIG. 1, in embodiments, processor 104 is configured to generate a testing framework 128 as a function of emulation parameters 120 and specification datum 112. As used throughout this disclosure, a “testing framework” is a set of test cases specifically tailored for the emulated environment and the hardware components being tested. In some embodiments, generating testing framework 128 may include generating automated testing scripts. In a non-limiting embodiment, emulation parameters may include settings and conditions under which the hardware or software is to be emulated, such as performance metrics, environmental conditions, user interactions and the like. Specification parameters may provide detailed descriptions of the hardware's expected functionalities, performance criteria, and operational limits. Testing framework may include specific test cases which may be designed to validate the hardware's performance and functionality against the emulation parameters and specification datums. Specific test cases may be customized to reflect the real-world demands and operating conditions expected of the hardware component. Testing framework 128 may include automated testing scripts which may be programmed to run tests automatically without manual intervention, to ensure that every aspect of the hardware is iteratively tested under consistent conditions. Testing framework may integrate with an emulated environment based on emulation parameters. Testing framework may include an iterative testing process to ensure that the framework results remain relevant and effective.

Continuing to refer to FIG. 1, in some embodiments, processor 104 may be configured to generate testing framework 128 using a framework machine learning model 152. As used herein, a “framework machine learning model” is a mathematical representation of the correlation between specification datum 112 and emulation parameters 120 with a testing framework. Framework machine learning model may be trained using framework training data. Framework training data may correlate inputs to outputs. In some embodiments, inputs may include specification datum, historical versions of specification datum, emulation parameters, historical versions of emulation parameters, digital twins, historical versions of digital twins, previous iterations of framework training data, examples of specification datum, examples of emulation parameters, examples of digital twins, and the like. In embodiments, framework machine learning model 152 may be configured to receive specification datum 112 and emulation parameters 120 as inputs and output testing framework 128. In a non-limiting example, framework machine learning model 152 may make predictions of test cases and testing environment based on previous correlations of similar emulation requirements and component specifications. Framework machine learning model 152 may include any machine learning model described throughout this disclosure. Framework machine learning model 152 include any methods and processes described with reference to FIGS. 2-4.

With continued reference to FIG. 1, in embodiments, processor 104 is configured to determine an integration datum 132 as a function of testing framework 128 and application datum 116. As used herein, an “integration datum” are test results related to how well a software application integrates with one or more hardware components. In embodiments, integration datum 132 may be configured to identify aspects of utilization of the digital twin prior to generation. In a non-limiting embodiment, configuration managed images of hardware and software may be traceable to managed hardware and software baselines. Integration datum may be used to identify and understand various aspects of the digital twin before its generation. Integration datum 132 may be configured to identify key attributes or characteristics of a software application, such as the digital twin, by including parameters related to the software application such as design specifications, operational parameters, and the like. In a non-limiting embodiment, integration datum 132 may measure potential changes or characteristics of a software application, such as the digital twin, and compare these measured characteristics or changes to a baseline which may serve as a standard for operability. In some embodiments, processor 104 may be further configured to generate integration datum 132 within deployment environment 144. In further embodiments, processor 104 may be further configured to generate integration datum 132 within containerized environment 148. In a non-limiting example, integration datum 132 may include a plurality of test results, where some may pass, while others may fail. In other examples, integration datum 132 may include an aggregation of all test results, such as a score showing a percentage of pass and failed tests. In an exemplary embodiment, integration datum 132 may be generated in real-time during simulating, testing, executing, and the like associated with the deployed software application. In some embodiments, integration datum 132 results may be used to modify testing framework 128.

where ai is attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes.

A two-dimensional subspace of a vector space may be defined by any two orthogonal vectors contained within the vector space. A vector's “norm’ is a scalar value, denoted ∥a∥ indicating the vector's length or size, and may be defined, as a non-limiting example, according to a Euclidean norm for an n-dimensional vector a as:

In an embodiment, and with continued reference to FIG. 1, each testing set may be represented by a dimension of a vector space; as a non-limiting example, each element of a vector may include a number representing an enumeration of co-occurrences of a first test, based on set conditions and components, represented by the vector with second test. Alternatively, or additionally, dimensions of vector space may not represent distinct testing set, in which case elements of a vector representing a first test may have numerical values that together represent a geometrical relationship to a vector representing a second test, wherein the geometrical relationship represents and/or approximates a semantic relationship between the first test and the second test. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below.

Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. In an embodiment associating testing sets to one another as described above may include computing a degree of vector similarity between a vector representing each test and a vector representing another test; vector similarity may be measured according to any norm for proximity and/or similarity of two vectors, including without limitation cosine similarity. As used in this disclosure “cosine similarity” is a measure of similarity between two-non-zero vectors of a vector space, wherein determining the similarity includes determining the cosine of the angle between the two vectors. Cosine similarity may be computed as a function of using a dot product of the two vectors divided by the lengths of the two vectors, or the dot product of two normalized vectors. For instance, and without limitation, a cosine of 0° is 1, wherein it is less than 1 for any angle in the interval (0,π) radians. Cosine similarity may be a judgment of orientation and not magnitude, wherein two vectors with the same orientation have a cosine similarity of 1, two vectors oriented at 90° relative to each other have a similarity of 0, and two vectors diametrically opposed have a similarity of −1, independent of their magnitude. As a non-limiting example, vectors may be considered similar if parallel to one another. As a further non-limiting example, vectors may be considered dissimilar if orthogonal to one another. As a further non-limiting example, vectors may be considered uncorrelated if opposite to one another. Additionally, or alternatively, degree of similarity may include any other geometric measure of distance between vectors.

Continuing to refer to FIG. 1, in some embodiments, vector database may be used for context insertion. As used herein, “context insertion,” is the process of incorporating additional information represented as numerical vectors. In embodiments, context insertion may include providing additional emulation parameters for generating testing framework 128. In other embodiments, context insertion may include automated artifacts 140, where the vector database is used to provide additional information for generating deployment environment 144. In a non-limiting example, context insertion may be used to provide additional data for generating deployment environment 144 based on testing framework 128.

With continued reference to FIG. 1, in some embodiments, processor 104 may be further configured to determine integration datum 132 using an integration machine learning model 156. An “integration machine learning model,” as used herein, is a machine-learning model as described in further detail below configured to receive application datum 116 and testing framework as inputs and output integration datum 132. In an embodiment, integration machine learning model may be trained with integration training data that comprises inputs correlated to outputs. In a non-limiting example, inputs used for integration training data may include application datum, historical versions of application datum, user-inputs, previous inputs of integration machine-learning model, examples of application datum, historical integration datums, and the like. Outputs to integration machine learning model may be used as inputs in future iterations. Integration machine learning model may be iteratively retrained with updated integration training data. Integration machine learning model may be trained as described in further detail within the disclosure. Integration machine learning model 156 may be consistent with any machine learning model as described with reference to FIGS. 3-5.

Still referring to FIG. 1, in some embodiments, processor 104 is configured to output a compatibility datum 136 as a function of integration datum 132. As used herein, a “compatibility datum” is a binary representation of whether the software application and hardware component being tested can be integrated with each other. In an embodiment, processor 104 may be configured to compare integration datum 132 to a set threshold. In a non-limiting example, processor 104 may compare the total of successful tests to a threshold, where compatibility datum 136 may be outputted as “compatible” even if some of the test results are negative. In embodiments, processor 104 may compare integration datum 132 to multi-level thresholds. A “multi-level threshold,” as used herein, are thresholds based on the importance level of each test. In a non-limiting example, some test cases may be marked as “high priority,” where even one failure might be above the threshold, while other test cases may be marked as “important,” where a lower threshold of failure is used.

Continuing to refer to FIG. 1, in some embodiments, at least a processor 104 may be further configured to generate at least an automated artifact 140. An “automated artifact,” as used herein, is an element of data generated through automated processes within an engineering development. In some embodiments, processor 104 is configured to generate automated artifact 140 as a function of integration datum. In additional embodiments, processor 104 may be further configured to generate automated artifact as a function of compatibility datum 136. In non-limiting examples, automated artifact 140 may include executable files, automated testing scripts, containerized environment 148 deployment files, test reports, test logs, automated code and testing documentation, configuration files, libraries, and dependency packages, and the like. One of ordinary skill in the art would appreciate that these are presented as way of example only and many other types of data may be included in automated artifact 140.

Still referring to FIG. 1, in an embodiment, at least a processor 104 may be configured to generate a deployment environment 144 as a function of emulation parameters 120. As used herein, a “deployment environment” is a configurable infrastructure configured to replicate real-world conditions for testing purposes. In a non-limiting example, deployment environment may include emulated flight scenarios and environmental conditions. In some embodiments, generating deployment environment 144 may include deploying a containerized environment 148. As used here, a “containerized environment” is an isolated and portable deployment environment 144. In some embodiments, containerized environment 148 may be one container. In a non-limiting example, containerized environment 148 may include a Docker container created by Docker, Inc. headquartered in Palo Alto, CA USA. In embodiments, containerized environment 148 may include a container orchestration system. In embodiments, container orchestration system may include the open-source Kubernetes orchestration system made by the Linux Foundation located in San Francisco, CA USA. In some embodiments, containerized environment 148 may include a container image. A “container image,” as described herein, is a snapshot or a packaged representation of an entire software state, including executable code, configurations, dependencies/libraries, and other required data. In some cases, container image may include source code, libraries, and other software components that the software relies on. In some cases, container image may include one or more configuration files which define a plurality of settings, parameters, and other configurations for the software. In some cases, configuration files may include certain OS configurations, environmental variables, or other system-level settings. In a non-limiting example container image may include a portable executable image combined with a manifest file that is used by a container manager as described below to deploy the container image on an operating environment with appropriate data services and restrictions. In some cases, container image may be used to package a software application with its entire collection of dependencies, ensuring that the software application can run consistently across different SOEs. Exemplary software applications may include, without limitation, flight management system (FMS) software, air traffic control (ATC) software, avionics software, electronic flight bag (EFB) software, ground support equipment software, weather forecasting and reporting software, cockpit display rendering software, and/or the like.

In some cases, container image may include a VM image that encapsulate a whole OS along with one or more pre-installed software applications. In embodiments, deployment environment 144 may be easily replicated across a plurality of host circuits e.g., servers or cloud environment. In other cases, container image may be used as a backup snapshot to restore/roll back system or a software application to a known working state. In a non-limiting embodiment, deployment environment 144 may implement programming changes made using emulation parameters into an original system, software system, hardware system, and the like. In another non-limiting environment, deployment environment may run several deployment programs independent each other, with each deployment environment running smoothly while being independent of another deployment environment.

In some embodiments, and continuing to refer to FIG. 1, processor 104 may be configured to be communicatively connected to a database. Database may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Database may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Database may include a plurality of data entries and/or records as described above. Data entries in a database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure.

Still referring to FIG. 1, processor 104 may be configured to display user interface using a display device 160. As used in the current disclosure, a “display device” is a device that is used to display a plurality of data and other digital content. Display device 160 may include a user interface. A “user interface,” as used herein, is a means by which a user and a computer system interact; for example, through the use of input devices and software. A user interface may include a graphical user interface (GUI), command line interface (CLI), menu-driven user interface, touch user interface, voice user interface (VUI), form-based user interface, any combination thereof, and the like. A user interface may include a smartphone, smart tablet, desktop, or laptop operated by the user. In an embodiment, the user interface may include a graphical user interface. In some embodiments, GUI may include icons, menus, other visual indicators, or representations (graphics), audio indicators such as primary notation, and display information and related user controls. A menu may contain a list of choices and may allow users to select one from them. A menu bar may be displayed horizontally across the screen such as pull-down menu. When any option is clicked in this menu, then the pulldown menu may appear. A menu may include a context menu that appears only when the user performs a specific action. An example of this is pressing the right mouse button. When this is done, a menu may appear under the cursor. Files, programs, web pages and the like may be represented using a small picture in a graphical user interface. For example, links to decentralized platforms as described in this disclosure may be incorporated using icons. Using an icon may be a fast way to open documents, run programs etc., because clicking on them yields instant access. Information contained in user interface may be directly influenced using graphical control elements such as widgets. A “widget,” as used herein, is a user control element that allows a user to control and change the appearance of elements in the user interface. In this context a widget may refer to a generic GUI element such as a check box, button, or scroll bar to an instance of that element, or to a customized collection of such elements used for a specific function or application (such as a dialog box for users to customize their computer screen appearances). User interface controls may include software components that a user interacts with through direct manipulation to read or edit information displayed through user interface. Widgets may be used to display lists of related items, navigate the system using links, tabs, and manipulate data using check boxes, radio boxes, and the like. User interface may be configured to display different levels of emulation parameters, digital twins, deployment environments, and the like. User interface may be able to show different levels of architectural and structural details of software, hardware, simulated systems, and the like.

Referring now to FIG. 2, an exemplary embodiment of a machine-learning module 200 that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data 204 to generate an algorithm instantiated in hardware or software logic, data structures, and/or functions that will be performed by a computing device/module to produce outputs 208 given data provided as inputs 212; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language.

where ai is attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes; this may, for instance, be advantageous where cases represented in training data are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values.

Continuing to refer to FIG. 2, computer, processor, and/or module may be configured to preprocess training data. “Preprocessing” training data, as used in this disclosure, is transforming training data from raw form to a format that can be used for training a machine learning model. Preprocessing may include sanitizing, feature selection, feature scaling, data augmentation and the like.

Further referring to FIG. 2, feature selection includes narrowing and/or filtering training data to exclude features and/or elements, or training data including such elements, that are not relevant to a purpose for which a trained machine-learning model and/or algorithm is being trained, and/or collection of features and/or elements, or training data including such elements, on the basis of relevance or utility for an intended task or purpose for a trained machine-learning model and/or algorithm is being trained. Feature selection may be implemented, without limitation, using any process described in this disclosure, including without limitation using training data classifiers, exclusion of outliers, or the like.

With continued reference to FIG. 2, feature scaling may include, without limitation, normalization of data entries, which may be accomplished by dividing numerical fields by norms thereof, for instance as performed for vector normalization. Feature scaling may include absolute maximum scaling, wherein each quantitative datum is divided by the maximum absolute value of all quantitative data of a set or subset of quantitative data. Feature scaling may include min-max scaling, in which each value X has a minimum value Xmin in a set or subset of values subtracted therefrom, with the result divided by the range of the values, give maximum value in the set or subset

X
    max
   
   :
      
   
    X
    
     n
     ⁢
     e
     ⁢
     w
    
   
  
  =
  
   
    
     X
     -
     
      X
      min
     
    
    
     
      X
      max
     
     -
     
      X
      min
     
    
   
   .

Feature scaling may include mean normalization, which involves use of a mean value of a set and/or subset of values, Xmean with maximum and minimum values:

X
   
    n
    ⁢
    e
    ⁢
    w
   
  
  =
  
   
    
     X
     -
     
      X
      
       m
       ⁢
       e
       ⁢
       a
       ⁢
       n
      
     
    
    
     
      X
      max
     
     -
     
      X
      min
     
    
   
   .

Feature scaling may include standardization, where a difference between X and Xmean is divided by a standard deviation σ of a set or subset of values:

X
    
     n
     ⁢
     e
     ⁢
     w
    
   
   =
   
    
     X
     -
     
      X
      
       m
       ⁢
       e
       ⁢
       a
       ⁢
       n
      
     
    
    σ
   
  
  .

Scaling may be performed using a median value of a set or subset Xmedian and/or interquartile range (IQR), which represents the difference between the 25th percentile value and the 50th percentile value (or closest values thereto by a rounding protocol), such as:

X
   
    n
    ⁢
    e
    ⁢
    w
   
  
  =
  
   
    
     X
     -
     
      X
      
       m
       ⁢
       e
       ⁢
       d
       ⁢
       i
       ⁢
       a
       ⁢
       n
      
     
    
    IQR
   
   .

Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various alternative or additional approaches that may be used for feature scaling.

Further referring to FIG. 2, computing device, processor, and/or module may be configured to perform one or more processes of data augmentation. “Data augmentation” as used in this disclosure is addition of data to a training set using elements and/or entries already in the dataset. Data augmentation may be accomplished, without limitation, using interpolation, generation of modified copies of existing entries and/or examples, and/or one or more generative AI processes, for instance using deep neural networks and/or generative adversarial networks; generative processes may be referred to alternatively in this context as “data synthesis” and as creating “synthetic data.” Augmentation may include performing one or more transformations on data, such as geometric, color space, affine, brightness, cropping, and/or contrast transformations of images. In a non-limiting example, data augmentation may be used for augmenting data with framework machine learning model 152, such as augmenting data related to specific testing sets within testing framework 128.

Further referring to FIG. 2, machine learning processes may include at least an unsupervised machine-learning processes 232. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes 232 may not require a response variable; unsupervised processes 232 may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like.

Further referring to FIG. 2, one or more processes or algorithms described above may be performed by at least a dedicated hardware unit 236. A “dedicated hardware unit,” for the purposes of this figure, is a hardware component, circuit, or the like, aside from a principal control circuit and/or processor performing method steps as described in this disclosure, that is specifically designated or selected to perform one or more specific tasks and/or processes described in reference to this figure, such as without limitation preconditioning and/or sanitization of training data and/or training a machine-learning algorithm and/or model. A dedicated hardware unit 236 may include, without limitation, a hardware unit that can perform iterative or massed calculations, such as matrix-based calculations to update or tune parameters, weights, coefficients, and/or biases of machine-learning models and/or neural networks, efficiently using pipelining, parallel processing, or the like; such a hardware unit may be optimized for such processes by, for instance, including dedicated circuitry for matrix and/or signal processing operations that includes, e.g., multiple arithmetic and/or logical circuit units such as multipliers and/or adders that can act simultaneously and/or in parallel or the like. Such dedicated hardware units 236 may include, without limitation, graphical processing units (GPUs), dedicated signal processing modules, FPGA or other reconfigurable hardware that has been configured to instantiate parallel processing units for one or more specific tasks, or the like, A computing device, processor, apparatus, or module may be configured to instruct one or more dedicated hardware units 236 to perform one or more operations described herein, such as evaluation of model and/or algorithm outputs, one-time or iterative updates to parameters, coefficients, weights, and/or biases, and/or any other operations such as vector and/or matrix operations as described in this disclosure. As used in this disclosure “matrix” is a rectangular array or table of numbers, symbols, expressions, vectors, and/or representations arranged in rows and columns. Matrix may be generated by performing a singular value decomposition function. As used in this disclosure a “singular value decomposition function” is a factorization of a real and/or complex matrix that generalizes the eigen decomposition of a square normal matrix to any matrix of m rows and n columns via an extension of the polar decomposition. For example, and without limitation singular value decomposition function may decompose a first matrix, A, comprised of m rows and n columns to three other matrices, U, S, T, wherein matrix U, represents left singular vectors consisting of an orthogonal matrix of m rows and m columns, matrix S represents a singular value diagonal matrix of m rows and n columns, and matrix VT represents right singular vectors consisting of an orthogonal matrix of n rows and n columns according to the function:

singular value decomposition function may find eigenvalues and eigenvectors of AAT and ATA. The eigenvectors of ATA may include the columns of IT, wherein the eigenvectors of AAT may include the columns of U. The singular values in S may be determined as a function of the square roots of eigenvalues AAT or ATA, wherein the singular values are the diagonal entries of the S matrix and are arranged in descending order. Singular value decomposition may be performed such that a generalized inverse of a non-full rank matrix may be generated.

given input x, a tanh (hyperbolic tangent) function, of the form

a tanh derivative function such as ƒ(x)=tanh2(x), a rectified linear unit function such as ƒ(x)=max(0, x), a “leaky” and/or “parametric” rectified linear unit function such as ƒ(x)=max(ax, x) for some a, an exponential linear units function such as

for some value of α (this function may be replaced and/or weighted by its own derivative in some embodiments), a softmax function such as

f
   ⁡
   (
   
    x
    i
   
   )
  
  =
  
   
    e
    x
   
   
    
     
      ∑
       
     
     i
    
    ⁢
    
     x
     i

where the inputs to an instant layer are xi, a swish function such as ƒ(x)=x*sigmoid(x), a Gaussian error linear unit function such as ƒ(x)=a(1+tanh(√{square root over (2/π)}(x+bxr))) for some values of a, b, and r, and/or a scaled exponential linear unit function such as

Now referring to FIG. 5, a method 500 of simulated virtual component testing is illustrated. In an embodiment, method 500, at step 505, includes receiving specification datum 112. Specification datum 112 may be received using any secured transmission described throughout this disclosure, such as containerized environment. In embodiments, specification datum 112 may be included in a hard drive communicatively connected to processor 104, Field Programmable Gate Array, Boot Operating Storage Solution Card, and the like. This may be implemented, without limitation, as described above with reference to FIGS. 1-4.

Continuing to refer to FIG. 5, at step 510, method 500 includes receiving application datum 116. Similar to above, application datum may be included in a hard drive, or any other storage medium, communicatively connected to processor 104. In embodiments, application datum 116 may be received through any secured transmission described herein. This may be implemented, without limitation, as described above with reference to FIGS. 1-4.

Still referring to FIG. 5, method 500, at step 515 includes generating emulation parameters 120 as a function of specification datum 112. In embodiments, determining emulation parameters 120 may include identifying engineering methodology 124. In an embodiment, emulation parameter may be configured to generate a digital twin. This may be implemented, without limitation, as described above with reference to FIGS. 1-4.

With continued reference to FIG. 5, at step 520, method 500 includes generating testing framework 128 as a function of emulation parameters 120. In some embodiments, method 500 may include generating a deployment environment 144. In embodiments, method 500 may include generating a deployment environment as a function of testing framework 128. In further embodiments, method 500 may further include generating a deployment environment 144 as a function of automated artifact 140. In a non-limiting example, automated artifact 140 may be used to generate deployment environment 144 for further testing of component, such as when re-running tests using same testing framework 128. In embodiments, method 500 may further include generating container 148. This may be implemented, without limitation, as described above with reference to FIGS. 1-4.

Continuing to refer to FIG. 5, method 500, at step 525, includes determining integration datum 132 as a function of testing framework 128 and application datum 116. In embodiments, method 500 may include determining integration datum 132 as a function of integration machine learning model 156. In some embodiments, method 500 may include storing integration datum 132 in an immutable sequential listing. Immutable sequential listings are described in more detail in reference to FIG. 5.

Still referring to FIG. 5, method 500, at step 530, includes outputting compatibility datum 136 as a function of integration datum 132. This may be implemented, without limitation, as described above with reference to FIGS. 1-4.

Continuing to refer to FIG. 5, method 500, at step 530, includes displaying a user interface using a display device. This may be implemented, without limitation, as described above with reference to FIGS. 1-4.

FIG. 6 shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system 600 within which a set of instructions for causing a control system to perform any one or more of the aspects and/or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and/or methodologies of the present disclosure. Computer system 600 includes a processor 604 and a memory 608 that communicate with each other, and with other components, via a bus 612. Bus 612 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures.

Computer system 600 may also include a storage device 624. Examples of a storage device (e.g., storage device 624) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device 624 may be connected to bus 612 by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device 624 (or one or more components thereof) may be removably interfaced with computer system 600 (e.g., via an external port connector (not shown)). Particularly, storage device 624 and an associated machine-readable medium 628 may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system 600. In one example, software 620 may reside, completely or partially, within machine-readable medium 628. In another example, software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In one example, a user of computer system 600 may enter commands and/or other information into computer system 600 via input device 632. Examples of an input device 632 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device 632 may be interfaced to bus 612 via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus 612, and any combinations thereof. Input device 632 may include a touch screen interface that may be a part of or separate from display 636, discussed further below. Input device 632 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

A user may also input commands and/or other information to computer system 600 via storage device 624 (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device 640. A network interface device, such as network interface device 640, may be utilized for connecting computer system 600 to one or more of a variety of networks, such as network 644, and one or more remote devices 648 connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network 644, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software 620, etc.) may be communicated to and/or from computer system 600 via network interface device 640.

Computer system 600 may further include a video display adapter 652 for communicating a displayable image to a display device, such as display device 636. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 652 and display device 636 may be utilized in combination with processor 604 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system 600 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 612 via a peripheral interface 656. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.