Searching code based on learned programming construct patterns and NLP similarity

An approach is provided to ingest software source code files into a question/answering (QA) system. During ingestion, source code blocks are classified to identify one or more constructs in the blocks as being domain-specific. Relationships between the blocks are then mapped. Software compliance regulations are ingested into the QA system. Using the QA system, a source code file is analyzed for compliance to the software compliance regulations. The analysis identifies code sections within the source code file as being domain-specific and subject to the ingested set of software compliance regulations.

BACKGROUND

Code reuse, also called software reuse, is the use of existing software, or software knowledge, to build new software. Code reuse has been in use from the earliest days of programming with programmers reusing sections of code, templates, functions, and procedures. The goal of code reuse is to save time and resources and reduce redundancy by taking advantage of code that has already been created. Code reuse implies the creation of a separately maintained version of the reusable code.

Some domains are heavily regulated for software programmers and complying with these regulations is difficult . Furthermore, mistakes made that lead to non-compliance have serious financial or legal consequences. For instance, in the medical domain the Food and Drug Administration (FDA) requires strict documentation of all medical algorithms used including a full “breadcrumb”/audit trail that describes exactly what evidence source was used to create the software algorithm/calculation. Thus, building a compliant solution in a regulated industry has two problems: #1 being able to find all instances of algorithms that need to be documented and #2 verifying that proof of documentation is present for each algorithm .

BRIEF SUMMARY

According to one embodiment of the present disclosure, An approach is provided to ingest software source code files into a question/answering (QA) system. During ingestion, source code blocks are classified to identify one or more constructs in the blocks as being domain-specific. Relationships between the blocks are then mapped. Software compliance regulations are ingested into the QA system. Using the QA system, a source code file is analyzed for compliance to the software compliance regulations. The analysis identifies code sections within the source code file as being domain-specific and subject to the ingested set of software compliance regulations

DETAILED DESCRIPTION

FIG. 1depicts a schematic diagram of one illustrative embodiment of a question/answer (QA) system100in a computer network102. QA system100may include knowledge manager104, which comprises one or more processors and one or more memories, and potentially any other computing device elements generally known in the art including buses, storage devices, communication interfaces, and the like. Computer network102may include other computing devices in communication with each other and with other devices or components via one or more wired and/or wireless data communication links, where each communication link may comprise one or more of wires, routers, switches, transmitters, receivers, or the like. QA system100and network102may enable question/answer (QA) generation functionality for one or more content users. Other embodiments may include QA system100interacting with components, systems, sub-systems, and/or devices other than those depicted herein.

QA system100may receive inputs from various sources. For example, QA system100may receive input from the network102, a corpus of electronic documents107or other data, semantic data108, and other possible sources of input. In one embodiment, some or all of the inputs to QA system100route through the network102and stored in knowledge base106. The various computing devices on the network102may include access points for content creators and content users. Some of the computing devices may include devices for a database storing the corpus of data. The network102may include local network connections and remote connections in various embodiments, such that QA system100may operate in environments of any size, including local and global, e.g., the Internet. Additionally, QA system100serves as a front-end system that can make available a variety of knowledge extracted from or represented in documents, network-accessible sources and/or structured data sources. In this manner, some processes populate the knowledge manager with the knowledge manager also including input interfaces to receive knowledge requests and respond accordingly.

In one embodiment, a content creator creates content in a document107for use as part of a corpus of data with QA system100. The document107may include any file, text, article, or source of data for use in QA system100. Content users may access QA system100via a network connection or an Internet connection to the network102, and may input questions to QA system100, which QA system100answers according to the content in the corpus of data. As further described below, when a process evaluates a given section of a document for semantic content, the process can use a variety of conventions to query it from knowledge manager104. One convention is to send a well-formed question.

Semantic data108is content based on the relation between signifiers, such as words, phrases, signs, and symbols, and what they stand for, their denotation, or connotation. In other words, semantic data108is content that interprets an expression, such as by using Natural Language Processing (NLP). In one embodiment, the process sends well-formed questions (e.g., natural language questions, etc.) to QA system100and QA system100may interpret the question and provide a response that includes one or more answers to the question. In some embodiments, QA system100may provide a response to users in a ranked list of answers.

In some illustrative embodiments, QA system100may be the IBM Watson™ QA system available from International Business Machines Corporation of Armonk, N.Y., which is augmented with the mechanisms of the illustrative embodiments described hereafter. The IBM Watson™ knowledge manager system may receive an input question which it then parses to extract the major features of the question, that in turn are then used to formulate queries that are applied to the corpus of data. Based on the application of the queries to the corpus of data, a set of hypotheses, or candidate answers to the input question, are generated by looking across the corpus of data for portions of the corpus of data that have some potential for containing a valuable response to the input question.

The IBM Watson™ QA system then performs deep analysis on the language of the input question and the language used in each of the portions of the corpus of data found during the application of the queries using a variety of reasoning algorithms. There may be hundreds or even thousands of reasoning algorithms applied, each of which performs different analysis, e.g., comparisons, and generates a score. For example, some reasoning algorithms may look at the matching of terms and synonyms within the language of the input question and the found portions of the corpus of data. Other reasoning algorithms may look at temporal or spatial features in the language, while others may evaluate the source of the portion of the corpus of data and evaluate its veracity.

The scores obtained from the various reasoning algorithms indicate the extent to which the potential response is inferred by the input question based on the specific area of focus of that reasoning algorithm. Each resulting score is then weighted against a statistical model. The statistical model captures how well the reasoning algorithm performed at establishing the inference between two similar passages for a particular domain during the training period of the IBM Watson™ QA system. The statistical model may then be used to summarize a level of confidence that the IBM Watson™ QA system has regarding the evidence that the potential response, i.e. candidate answer, is inferred by the question. This process may be repeated for each of the candidate answers until the IBM Watson™ QA system identifies candidate answers that surface as being significantly stronger than others and thus, generates a final answer, or ranked set of answers, for the input question. More information about the IBM Watson™ QA system may be obtained, for example, from the IBM Corporation website, IBM Redbooks, and the like. For example, information about the IBM Watson™ QA system can be found in Yuan et al., “Watson and Healthcare,” IBM developerWorks, 2011 and “The Era of Cognitive Systems: An Inside Look at IBM Watson and How it Works” by Rob High, IBM Redbooks, 2012.

Types of information handling systems that can utilize QA system100range from small handheld devices, such as handheld computer/mobile telephone110to large mainframe systems, such as mainframe computer170. Examples of handheld computer110include personal digital assistants (PDAs), personal entertainment devices, such as MP3 players, portable televisions, and compact disc players. Other examples of information handling systems include pen, or tablet, computer120, laptop, or notebook, computer130, personal computer system150, and server160. As shown, the various information handling systems can be networked together using computer network102. Types of computer network102that can be used to interconnect the various information handling systems include Local Area Networks (LANs), Wireless Local Area Networks (WLANs), the Internet, the Public Switched Telephone Network (PSTN), other wireless networks, and any other network topology that can be used to interconnect the information handling systems. Many of the information handling systems include nonvolatile data stores, such as hard drives and/or nonvolatile memory. Some of the information handling systems shown inFIG. 1depicts separate nonvolatile data stores (server160utilizes nonvolatile data store165, and mainframe computer170utilizes nonvolatile data store175. The nonvolatile data store can be a component that is external to the various information handling systems or can be internal to one of the information handling systems. An illustrative example of an information handling system showing an exemplary processor and various components commonly accessed by the processor is shown inFIG. 2.

FIG. 2illustrates information handling system200, more particularly, a processor and common components, which is a simplified example of a computer system capable of performing the computing operations described herein. Information handling system200includes one or more processors210coupled to processor interface bus212. Processor interface bus212connects processors210to Northbridge215, which is also known as the Memory Controller Hub (MCH). Northbridge215connects to system memory220and provides a means for processor(s)210to access the system memory. Graphics controller225also connects to Northbridge215. In one embodiment, PCI Express bus218connects Northbridge215to graphics controller225. Graphics controller225connects to display device230, such as a computer monitor.

Northbridge215and Southbridge235connect to each other using bus219. In one embodiment, the bus is a Direct Media Interface (DMI) bus that transfers data at high speeds in each direction between Northbridge215and Southbridge235. In another embodiment, a Peripheral Component Interconnect (PCI) bus connects the Northbridge and the Southbridge. Southbridge235, also known as the I/O Controller Hub (ICH) is a chip that generally implements capabilities that operate at slower speeds than the capabilities provided by the Northbridge. Southbridge235typically provides various busses used to connect various components. These busses include, for example, PCI and PCI Express busses, an ISA bus, a System Management Bus (SMBus or SMB), and/or a Low Pin Count (LPC) bus. The LPC bus often connects low-bandwidth devices, such as boot ROM296and “legacy” I/O devices (using a “super I/O” chip). The “legacy” I/O devices (298) can include, for example, serial and parallel ports, keyboard, mouse, and/or a floppy disk controller. The LPC bus also connects Southbridge235to Trusted Platform Module (TPM)295. Other components often included in Southbridge235include a Direct Memory Access (DMA) controller, a Programmable Interrupt Controller (PIC), and a storage device controller, which connects Southbridge235to nonvolatile storage device285, such as a hard disk drive, using bus284.

ExpressCard255is a slot that connects hot-pluggable devices to the information handling system. ExpressCard255supports both PCI Express and USB connectivity as it connects to Southbridge235using both the Universal Serial Bus (USB) the PCI Express bus. Southbridge235includes USB Controller240that provides USB connectivity to devices that connect to the USB. These devices include webcam (camera)250, infrared (IR) receiver248, keyboard and trackpad244, and Bluetooth device246, which provides for wireless personal area networks (PANs). USB Controller240also provides USB connectivity to other miscellaneous USB connected devices242, such as a mouse, removable nonvolatile storage device245, modems, network cards, ISDN connectors, fax, printers, USB hubs, and many other types of USB connected devices. While removable nonvolatile storage device245is shown as a USB-connected device, removable nonvolatile storage device245could be connected using a different interface, such as a Firewire interface, etcetera.

Wireless Local Area Network (LAN) device275connects to Southbridge235via the PCI or PCI Express bus272. LAN device275typically implements one of the IEEE .802.11 standards of over-the-air modulation techniques that all use the same protocol to wireless communicate between information handling system200and another computer system or device. Optical storage device290connects to Southbridge235using Serial ATA (SATA) bus288. Serial ATA adapters and devices communicate over a high-speed serial link. The Serial ATA bus also connects Southbridge235to other forms of storage devices, such as hard disk drives. Audio circuitry260, such as a sound card, connects to Southbridge235via bus258. Audio circuitry260also provides functionality such as audio line-in and optical digital audio in port262, optical digital output and headphone jack264, internal speakers266, and internal microphone268. Ethernet controller270connects to Southbridge235using a bus, such as the PCI or PCI Express bus. Ethernet controller270connects information handling system200to a computer network, such as a Local Area Network (LAN), the Internet, and other public and private computer networks.

FIGS. 3-6depict an approach that uses a question answering (QA) system to analyze software code based on learned programming construct patterns and natural language processing (NLP) similarities to compliance regulations. Compliance requirements are affecting an increasing number of business processes. Many specialized processes are being designed solely to meet industry specific regulations. As various compliance initiatives become more intertwined from regulatory and organizational perspectives, the use of multiple and disparate compliance solutions can lead to duplicate, and often contradictory, processes and documentation. Such environments do not provide clear visibility into organizations' risk and compliance profiles. They also reduce efficiency, and escalate the cost of compliance.

Using the approach provided herein, software code is ingested into the QA system for a particular domain, such as software dealing with health care and patient records. In addition, regulations, such as laws, policies, and the like, promulgated by a government regulatory body or other compliance organization, are also ingested into the QA system. With the base of knowledge ingested into the QA system, software code being developed for the domain, such as a new or improved patient-management system for a hospital, is analyzed based upon the ingested base of knowledge including the previously ingested software code for the domain as well as the previously ingested software compliance regulations. During analysis, NLS questions are formulated based on the various constructs found in the software code that is being analyzed. Using the patient software example, a construct such as routine named “Retrieve_Patient_Record” would be selected and a NLS question formulated such as “Is the ‘Retrieve_Patient_Record’ routine subject to any health care regulations?” This question is submitted to the QA system that has a knowledge base of other health care software programs and the health care regulations that were previously ingested. The QA system responds with an answer and a confidence level.

For example, the above-question may receive an answer that the routine is subject to a particular health care regulation with a confidence score of 95 out of a possible 100, indicating a high confidence that the software routine is subject to the cited regulation. The QA system is able to provide the regulation and a high confidence value when other software previously ingested programs have routines with the same or similar names that were noted as being subject to the cited regulation. If other software programs do not have similar routine names, then the QA system can parse the question into component parts with the result being that the routine deals with “patient names” and can also obtain a high confidence value by comparing the words and phrases to words and phrases found in the compliance regulations. In this case, the QA system would identify the cited regulation based on the ingested compliance regulations. Using a combination of both the previously ingested software code and the ingested software compliance regulations further bolster the QA system's confidence level that the posed question is subject to the cited regulation.

In one embodiment, multiple answers can be returned by the QA system with each answer having its own confidence level. Using the example from above, the QA system may find a first regulation dealing with the handling of patient names and calculate a confidence value of 95/100, a second regulation dealing with patient confidentiality and calculate a confidence value of 88/100, and a third regulation dealing with inter-hospital communications and assign a lower confidence value of 35/100. A threshold can be applied so that candidate answers with lower scores are filtered out. In this case, if a threshold value of 75 is established then the first and second regulations would be noted as likely applying to the selected routine dealing with retrieving patient records, and the third regulation would be filtered out as it is unlikely that the routine, with the given data, deals with inter-hospital communication. Of course, other variables and programming constructs in the routine may increase or decrease the confidence value pertaining to each of the regulations and may further result in the inclusion of other compliance regulations.

FIG. 3is an exemplary diagram showing processes and data stores utilized to search software source code based on learned programming construct patterns and natural language processing (NLP) similarities. Question answering (QA) system100includes knowledge base106, also referred to as a “corpus” that is used by QA Engine104to answer submitted questions.

The domain that is being addressed, such as health care, often has one or more regulatory organizations, such as the FDA, that promulgate software compliance regulations320. Software developed for the domain (e.g., health care, etc.) is subject to these software compliance regulations. For example, certain documentation may be required when software being developed by the software developer performs certain tasks. In the health care industry, certain documentation is required when software performs certain tasks related to patient identifies, such as names, identification numbers, and the like. In the case of health care, such regulations are often enacted to protect patients' identify and their privacy, such as ailments patients have experienced or treatments that the patients have received. Process325ingests the regulations promulgated by the regulatory organization(s). After ingestion, the compliance regulations are depicted as data store330which is part of the QA system's corpus (knowledge base106).

In many industries, such as health care, many software programs have already been written that were previously made to comply with the software compliance regulations. Archive340depicts a body of source code previously written, and documented, that performs tasks and functions in the regulated industry. In the health care field, such tasks and functions would include handling of patient records and other health care related activities. Predefined process350ingests archive340in order to build a domain-specific base of source code knowledge (seeFIG. 4and corresponding text for processing details). After ingestion, the base of knowledge derived from the domain-specific source code are depicted as data store360which is part of the QA system's corpus (knowledge base106). At this point, QA system100has been provided with domain-specific software compliance regulations (data store330) as well as a base of source code knowledge that has already been written for the domain (data store360).

QA system100is now ready to answer questions pertaining to new software programs being written for the domain. Predefined process370analyzes new source code file380(seeFIG. 5and corresponding text for processing details). As shown in greater detail inFIG. 5, predefined process370analyzes constructs included in source code file380and poses natural language questions to QA system100with the questions focused on whether various constructs and relationships (code sections) found in new source code file380are domain-specific (e.g., directed at the health care domain, etc.) and whether such code sections are subject to the software compliance regulations330previously ingested into the QA system's knowledge base106. Armed with the regulations and the source code archive, the QA system is able to respond with an answer as to whether the code section is domain-specific and whether such code section is further subject to the software compliance regulations. Regulatory documentation390is output from predefined process370providing details as to the code sections in the new source code that are subject to the software compliance regulations. In one embodiment, regulatory documentation390includes specific citations, or references, to particular regulations. For example, in the health care domain, a software routine that retrieves a patient record would likely be subject to one or more regulations promulgated by the FDA.

FIG. 4is an exemplary flowchart showing steps performed to build a domain specific base of source code knowledge.FIG. 4processing commences at400and shows the steps taken by a process that performs a routine that builds a domain-specific base of source code knowledge and ingests the domain-specific knowledge to a corpus, or knowledge base, utilized by a question answering (QA) system. At step405, the process selects the first source code file (e.g., program, etc.) from source code archive340. The selected source code and any metadata pertaining to the selected source code are stored in memory area410. At step415, the process selects the first block of code from the selected source code. The selected block, also referred to as a “code section,” is written to memory area420.

At step425, the process classifies the selected block based on the operation(s) performed by the block. For example, the selected block could be a conditional assignment, a manipulation routine, a transformer function, a routing function, and the like. The classification of the selected code section is written to memory area420. In one embodiment, the following process is used to classify code:1) Use prior art to classify all blocks/methods;2) Blocks are classified:a) as conditional assignments;b) manipulation (bunch of sets/assignment on the same object, no relationships);c) transformer (reading values from one object, setting (assignment) the value on another object); andd) routing (reading values from one object, calling a separate function/method based on condition).3) Based on classification determine the object types and whether any of them are in the medical domain by lexical classification or manual annotation, or NLP parsing of comments;4) Determine the object types in the code and their relationships (affects, conditionally determines, routes to);5) Match the entity relationships to corpora for the domain:a) If they match assign a weight based on the type of block and the relationship match;b) Assign weight based on the property type match and the classification types (assignment has similar or same property in code and corpora); andc) If they do not match assign a lower weight.6) Based on the weighting and the object types match in the corpora determine whether it should be deemed within the requirement (FDA medical algorithms and how much); and7) Show blocks of code identified as potential FDA Medical algorithm with code reasoning based on classification and match with evidence to corpora statements and annotations (tuple, relationships) from corpora.

For example, in a medical context, a medical term, such as “anemia” might be found in the code, such as a programming statement like:

Here, the term “anemia,” a medically relevant term, is found in the code and any software compliance guidelines regarding such term would be found and processed accordingly.

At step430, the process identifies the code section as being either domain-specific or general purpose. The identification of the code section is also written to memory area420. In one embodiment, the selected code section is searched for keywords pertaining to the domain. If any such keywords are found, then the block is identified as domain-specific, otherwise the block is identified as a general purpose code section. In a further embodiment, non-program language terms (e.g., variables, function names, etc.) are compared to such non-program language terms found in previously ingested source code files. Furthermore, in this embodiment, the QA system can be utilized to ask natural language questions such as “is the term ‘patient_name’ domain specific?” with the QA system answering the question based upon the extent of the knowledge base of already-ingested source code files.

At step435, the process selects the first program construct in the selected code section (e.g., variable, class, etc.). The selected program construct is stored in memory area440. At step445, the process identifies the selected construct as either being domain-specific or general purpose with the identification being performed in a similar fashion as the identification of the code section itself (e.g., utilizing keywords, comparing such constructs to previously ingested constructs from other source code files, etc.). The identification of the construct as being either domain-specific or general purpose is also stored in memory area440. The process determines as to whether there are more program constructs in the selected code section to process (decision450). If there are more program constructs in the selected code section to process, then decision450branches to the ‘yes’ branch which loops back to select and process the next program construct from the selected code section. This looping continues until there are no more program constructs in the selected code section to process, at which point decision450branches to the ‘no’ branch for further processing.

At step455, the process maps relationships between the selected code section and other code sections. For example, the selected code section may call or reference another code section or the selected code section may be a function or procedure that is called or referenced by other code sections. Such relationship data between code sections is stored in memory area460.

The process determines as to whether there more additional code sections in the selected source code file to process (decision465). If there are more additional code sections in the selected source code file to process, then decision465branches to the ‘yes’ branch which loops back to select and process the next code section as described above. This looping continues until there are no more code sections in the selected source code file to process, at which point decision465branches to the ‘no’ branch and processing continues.

At step470, the process ingests the source code from memory area410, the code section classification data from memory area420, the program construct data from memory area440, and the relationship data from memory area440into the QA system's corpus, or knowledge base,106. The process then determines as to whether there are more source code files in archive340to process and ingest as described above (decision475). If there are more source code files to process and ingest, then decision475branches to the ‘yes’ branch which loops back to select and process the next domain-specific source code file. This looping continues until there are no more source code files to process, at which point decision475branches to the ‘no’ branch at which point processing ends at495.

FIG. 5is an exemplary flowchart that performs steps to comply with various regulations related to software that is being constructed.FIG. 5processing commences at500and shows the steps taken by a process that performs a routine that analyzes a new source code file being developed for a domain and identifies regulations that apply to the source code file.

At step505, the process formulates a question pertaining to the new source code file as being domain specific and whether the new source code file is subject to domain specific regulations. At predefined process510, the process performs the Process Formulated Question by QA System Routine (seeFIG. 6and corresponding text for processing details).

At step515, the process selects the first code section from new source code file380. At step520, the process classifies the selected code section similar to the classification step425shown inFIG. 4(e.g., conditional assignments, manipulation, transformer, routing, etc.). At step525, the process formulates a question pertaining to the selected code section and the code section classification as being domain specific and whether the code section is subject to domain specific regulations. At predefined process530, the process again performs the Process Formulated Question by QA System Routine (seeFIG. 6and corresponding text for processing details), this time processing the question formulated in step525.

At step535, the process selects the first programming construct from the selected code section (e.g., variable, class, etc.). At step540, the process formulates a question pertaining to the selected programming construct being domain specific and whether the selected program construct is subject to domain specific regulations. At predefined process545, the process again performs the Process Formulated Question by QA System Routine (seeFIG. 6and corresponding text for processing details), this time processing the question formulated in step540.

The process determines as to whether there are more program constructs in the selected code section to process (decision550). If there are more program constructs in the selected code section to process, then decision550branches to the ‘yes’ branch which loops back to select and process the next program construct from the selected code section. This looping continues until there are no more program constructs to process, at which point decision550branches to the ‘no’ branch for further processing.

At step555, the process maps the relationships between the selected code section and other code sections or routines. For example, the selected code section may call or reference other code sections or routines. In addition, the selected code section may be a routine or function that is called or referenced by other code sections. At step560, the process formulates question(s) pertaining to the mapped relationships as being domain specific and whether such relationships are subject to domain specific regulations. At predefined process565, the process again performs the Process Formulated Question by QA System Routine (seeFIG. 6and corresponding text for processing details), this time processing the question formulated in step560.

The process determines as to whether there are more code sections in the new source code file to process (decision570). If there are more code sections in the new source code file to process, then decision570branches to the ‘yes’ branch which loops back to select and process the next code section as described above. This looping continues until there are no more code sections to process, at which point decision570branches to the ‘no’ branch and processing ends at595.

FIG. 6is an exemplary flowchart that processes formulated questions pertaining to code using a question/answering (QA) system.FIG. 6processing commences at600and shows the steps taken by a process that performs a routine that processes a formulated question using the QA system. At step605, the process submits the formulated question to the QA System where it is processed by QA system pipeline310.

QA system pipeline includes a number of processes that break the submitted question down in order to search the QA system's knowledge base106for an answer to the submitted question. As shown inFIG. 5, the submitted questions are formulated in a natural language format, such as “is the term ‘patient_record’ a domain specific term?” and the like depending on the aspect of the source code file that is being analyzed. At step615, the QA system pipeline commences and first performs a question and topic analysis. Next, at step620, the QA system pipeline performs a question decomposition process. At step625, the QA system pipeline next performs a primary search for the submitted question using knowledge base106. At step630, the QA system pipeline then generates candidate answers to the submitted question. At step635, the QA system pipeline scores the candidate answers. At step640, the QA system pipeline retrieves supporting evidence from the knowledge base (corpus). At step645, the QA system pipeline performs a deep evidence scoring process using the knowledge base. At step650, the QA system pipeline performs a final merging and ranking resulting in one or more candidate answers as well as a confidence value pertaining to the various candidate answers.

Candidate answers with low confidence values are filtered out at this point in the process. The answers and their respective confidence values are stored in data store360. In addition, references to specific regulations are written to data store355along with the respective code section(s) to which the regulations apply.

At step665, the process receives the response from the QA system. The response includes answers, confidence values, and supporting evidence as well as the cited regulations and the code sections, constructs, or relationships to which the regulations apply. The process determines as to whether the answer's confidence value is greater than an established threshold (decision670). If the answer's confidence value is greater than an established threshold, then decision670branches to the ‘yes’ branch whereupon, at step675, the step writes the code section and/or construct and the domain-specific regulation to required regulatory documentation390with the domain-specific regulation being the regulation, or regulations, that apply to the code section, constructs, or relationships. On the other hand, if the answer's confidence value is less than the established threshold or the answer indicates that the code section, construct, or relationship are general purpose and not domain specific, then decision670branches to the ‘no’ branch bypassing step675. Processing then returns to the calling routine (seeFIG. 5) at695.