Systems and methods for providing software components for developing software applications

This disclosure relates to methods and systems for providing software components for developing software applications. In one embodiment, a method for providing software components for developing software applications is provided. The method comprises receiving user input requirements associated with the software application; determining a requirements matching score for every software component existing in an application development environment, based on a comparison between the received requirements and a requirements model, wherein the requirements model is generated based on historic user requirements and usage; determining a performance score based on a response time associated with the software components; determining weights corresponding to the requirements matching score and the performance score based on the requirements matching score; determining a combined score based on the determined scores and associated weights; selecting software components for developing the software application based on the determined combined scores; and providing the selected software components to the user.

TECHNICAL FIELD

This disclosure relates generally to development of software applications, and more particularly, to methods and systems for providing software components for developing software applications.

BACKGROUND

In a software development organization, various software developers develop source codes and software components for implementing various types of functionalities and software applications. Reusing the developed software components can enable faster implementation of new software applications and would be a step forward towards rapid application development (RAD).

There are several challenges in achieving RAD using existing systems due to various technical problems in reusing the developed software components. In some existing systems, there is no search functionality of developed software components based on business logic. In some existing systems, there is no search functionality of developed software components based on technical requirements of the new application. Further, in some existing systems, there is no learning mechanism which can recommend software components for application development with minimal developer assistance.

SUMMARY

Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems in conventional software application development.

Embodiments of the present disclosure provide systems and methods for providing software components for developing software applications. In one embodiment, a system for providing one or more software components for developing a software application is provided. The system comprises one or more hardware processors and one or more memory units storing instructions executable by the one or more hardware processors for receiving a user input via a user interface, the input including one or more requirements associated with the software application; determining, for each of a plurality of software components existing in an application development environment, a requirements matching score based on a comparison between the received requirements and a requirements model associated with the corresponding software component, wherein the requirements model is generated based on historic user requirements and historic usage of the software component; determining, a performance score for each of the plurality of software components based on a response time associated with the corresponding software component; determining, weights corresponding to the requirements matching score and performance score based on the requirements matching score; determining, a combined score for each of the plurality of software components based on the determined score values and associated weights; selecting, from the plurality of software components, the one or more software components for developing the software application based on the combined score for each of the plurality of software components; and providing, the one or more software components to the user via the user interface.

In another embodiment, a method for providing one or more software components for developing a software application is provided. The method comprises: receiving, by a processor, a user input via a user interface, the input including one or more requirements associated with the software application; determining, by the processor, for each of a plurality of software components existing in an application development environment, a requirements matching score based on a comparison between the received requirements and a requirements model associated with the corresponding software component, wherein the requirements model is generated based on historic user requirements and historic usage of the software component; determining, by the processor, a performance score for each of the plurality of software components based on a response time associated with the corresponding software component; determining, by the processor, weights corresponding to the requirements matching score and performance score based on the requirements matching score; determining, by the processor, a combined score for each of the plurality of software components based on the determined score values and associated weights; selecting, by the processor, from the plurality of software components, the one or more software components for developing the software application based on the combined score for each of the plurality of software components; and providing, by the processor, the one or more software components to the user via the user interface.

In yet another embodiment, a non-transitory computer readable storage medium is provided. The storage medium stores a program that, when executed by a computer, cause the computer to perform a method of receiving a user input via a user interface, the input including one or more requirements associated with the software application; determining, for each of a plurality of software components existing in an application development environment, a requirements matching score based on a comparison between the received requirements and a requirements model associated with the corresponding software component, wherein the requirements model is generated based on historic user requirements and historic usage of the software component; determining, a performance score for each of the plurality of software components based on a response time associated with the corresponding software component; determining, weights corresponding to the requirements matching score and performance score based on the requirements matching score; determining, a combined score for each of the plurality of software components based on the determined score values and associated weights; selecting, from the plurality of software components, the one or more software components for developing the software application based on the combined score for each of the plurality of software components; and providing, the one or more software components to the user via the user interface.

DETAILED DESCRIPTION

The embodiments described herein provide systems and methods for providing software components for developing software applications. As described above, there are several technical problems in conventional system and methods for providing software components for developing software applications. The disclosed systems and methods include a development environment where many software components may be developed and deployed as application program interfaces (APIs) or services or libraries. Such software components developed for an organization can have a common structure which may include, for example, the component type name, algorithms supported, database layer supported, ontologies etc.

Metadata can be created for every software component as soon as it is registered in the repository. The metadata may include various functional and technical details. Based on the user requirement (functional and/or technical), an API/software component can be selected based on a component selection model. The disclosed system can suggest one or more options with an associated score to the user. The user may then choose from one or more of the suggested options. Based on the user selection, rewards may be added to the selected software components and the component selection model can be updated with details of the learning. The received rewards can be used in calculation of matching scores for future suggestions to the user.

Disclosed methods and systems can learn from developer actions (such as selection of API, creation of API work flow, requirement and solution mapping). Further, disclosed methods and systems can suggest and recommend solution blocks using the underlying previously captured requirement to solution mapping. In some embodiments, disclosed methods and systems may analyze the underlying logs of previously created solutions and recommend a combination of blocks that has the best performance. In some embodiments, disclosed methods and systems can track the performance of APIs such as time utilized, amount of data handled, failure frequency, usage numbers, and calculate the success rate of each API. Further, disclosed methods and systems can understand the inputted user requirements and create solutions and data models.

FIG. 1illustrates an exemplary system100for providing software components for developing software applications using disclosed systems and methods. Exemplary system100can comprise user interface110, component recommender120, data model builder130, component assembler140, component selection module150. Component selection module150may further comprise component data model160, component scoring module170and component metadata repository180.

For the purposes of this disclosure, “modules” may be implemented in software, hardware, firmware, a mix of any of those, or the like. For example, if the disclosed “modules” are implemented in software, they may be stored in a memory associated with system100. Processors, I/O devices, and memory devices may be used to perform processes to implement and facilitate operations of the modules. Thus, the modules may include code instructions executable by one or more processors, alone or in various combinations with other modules disclosed in this or other embodiments. If the disclosed “modules” are implemented in hardware, they may comprise an embedded system or other dedicated hardware configured by machine code, assembly code, or the like to interact with other modules to perform functions consistent with disclosed embodiments.

Component metadata repository180can include the details of the components or APIs registered to system100. In some embodiments, component metadata repository180can include the functional and technical details of the registered APIs regarding their usage.

Component data model160can be a data model built on the basis of past usage of components, of system100based on user requirements. Component data model160can be updated each time more usage data becomes available.

Component scoring module170can calculate a score for the component or API selected for a requested requirement. Components with a score above certain threshold values can be displayed to the user. In some embodiments, a breakup of the score components may also be displayed to the user.

Component recommender120may perform two functions. One function may be to receive a user input and communicate the received input to component selection module150. A second function may be to display component recommendations to the user based on the component score received from component selection module150. In some embodiments, component recommender120may also display a breakup of the score components. A user can either accept the component suggestion provided by component recommender120or choose a different component via the user interface110.

Component assembler140can assemble the software components based on the user selection. In some embodiments, component assembler140can also update component data model160according to the user selection.

Data model builder130can create a data model based on the user input data set, type of data model required, and the parameters of the data model. The types of data model can include, for example, clustering, classification, regression and dimensionality reduction. In some embodiments, data model builder130can create a data model based on parameters that may include, for example, optimization, LF, regularization, and activation function.

FIG. 2illustrates an exemplary method200for providing software components for developing software applications. Referring toFIG. 2, it will be readily appreciated by one of ordinary skill in the art that the illustrated procedure can be altered to modify the order of steps, delete steps or further include additional steps. In some embodiments, method200can be performed, for example, by system100ofFIG. 1.

After an initial start, user interface110can receive from a user, requirements associated with development of a software application in step205. The received user requirements may be in text format and can include functional and/or technical requirements. In some embodiments, the technical requirements may include preferred operating systems, algorithms, multithreads, etc. In some embodiments, the received user requirements may also include the dataset on which the solution has to be provided. As an example, a user may upload a dataset, specify the type of modeling required (examples can be clustering, classification, regression and dimensionality reduction) and specify the parameters of the modeling (examples can be optimization, LF, regularization, and activation function).

In step210, component recommender120can analyze the received user requirements. Component recommender120can determine if the received user requirements include all mandatory requirements. If any mandatory requirements are determined to be missing, component recommender120can request the user to input the missing requirements through user interface110. If all mandatory requirements are determined to be present, component recommender120can send the received requirements to component selection module150.

In step215, component selection module150can search components in component metadata repository180based on the user requirements received from component recommender120. Component selection module150can calculate a score for the components based on the received user requirements.

In step220, component scoring module170of component selection module150can calculate a combined score for the searched components from step215. If the calculated combined score is above a threshold value, component selection module150can provide that component to component recommender120for display to the user.

In some embodiments, the combined score may comprise requirements matching score, functional and technical requirements matching score, performance score, and success rate score. The individual score components may each have different weightage and combined score can be calculated based on the value and weightage of the individual scores. In some embodiments, the weightage of the individual scores can be based on rules set for the requirements matching score. The requirements matching score can be computed first and the weightage of the individual scores (including the requirements matching score) can be decided based on the computed value of the requirements matching score. The requirements matching score can be computed by analyzing and comparing the received user requirements with an associated requirements model built based on ontology, natural language processing (NLP) techniques, historic user requirements and usage data. As an example, if the requirements matching score is computed to be zero, that can suggest that there are no matching input requirements in historical data and zero weightage can be assigned to the requirements matching score. For another example, where the computed requirements matching score is a high value, indicating high similarity with one or more historical user requirements, a higher weightage can be assigned to the requirements matching score. In some embodiments, rules can be configured to have different weightage for the individual scores according to user input.

In some embodiments, if the requirements matching score is computed to be zero, then weightage of the functional and technical requirements matching score can be set at 0.7, weightage of the performance score can be set at 0.2 and weightage of the success rate score can be set at 0.1. If the requirements matching score is computed to be between one and six, then weightage of the requirements matching score can be set at 0.3, weightage of the functional and technical requirements matching score can be set at 0.3, weightage of the performance score can be set at 0.3 and weightage of the success rate score can be set at 0.1. Further, if the requirements matching score is computed to be between six and eight, then weightage of the requirements matching score can be set at 0.7, weightage of the functional and technical requirements matching score can be set at 0.2, weightage of the performance score can be set at 0.1 and weightage of the success rate score can be set at zero. If the requirements matching score is computed to be between eight and ten, then weightage of the requirements matching score can be set at 0.8, weightage of the functional and technical requirements matching score can be set at 0.1, weightage of the performance score can be set at 0.05 and weightage of the success rate score can be set at 0.05.

In some embodiments, the component scoring module170can calculate the functional and technical requirements matching score using ontology based search for input text in received functional and technical requirement details and searching for matching text in component metadata repository180. Component scoring module170can average the results of functional and technical matching in order to compute the functional and technical requirements matching score.

In some embodiments, the component scoring module170can calculate the performance score by only considering response time and assuming other performance parameters like processor speed, available memory, hard disk and bandwidth are the same. The response time may be determined as the time from when a user enters a request until the first character of the response is received. In calculating the response time, two sets of data can be considered. One set of data can be the maximum time taken for unit size of data.FIG. 3is a table of exemplary set of response time data and associated response time calculations, in accordance with some embodiments of the present disclosure. Column310may list names of exemplary APIs or components. Column320may list maximum time taken for a response and column330may list size of associated data for each API or component in column310. Column340lists the calculated maximum response time for unit size of data. A second set of data for response time calculations can be the minimum time taken for unit size of data. Referring back toFIG. 3, column350may list minimum time taken for a response and column360may list size of associated data for each API or component in column310. Column370lists the calculated minimum response time for unit size of data. The performance score can be calculated by averaging and normalizing the two sets of data. For example, column380ofFIG. 3may be calculated by averaging the maximum response time for unit size of data340and minimum response time for unit size of data370. Column390may be calculated by normalizing average response time value380for each API/component by the sum of all average response time values395.

In some embodiments, the component scoring module170can calculate the success rate score for an API or component based on the number of times that API or component was used or called and the number of times it was completed.FIG. 4is a table of exemplary set of usage and success data of various APIs or components and associated success rate calculations, in accordance with some embodiments of the present disclosure. Column410may list names of exemplary APIs or components and column420may list the number of times the exemplary API or component was used. Further, column430may list the number of failures and column440may list number of successes for each API or component in column410. Column450may list the success rate calculated as the ratio of number of successes440to the total number of usage420.

Component scoring module170can calculate a combined score using the computed requirements matching score, functional and technical requirements matching score, performance score, and success rate score and their corresponding weightage. The combined score can be calculated using the following expression:
Combined score=(Requirements matching score×weightage of requirements matching score)+(functional and technical requirements matching score×weightage of functional and technical requirements matching score)+(performance score×weightage of performance score)+(success rate score×weightage of success rate score)

In an embodiment, if the API/component may be selected by a user which is not selected by the component selection module150, then the component scoring module170may calculate an additional score for that API/component. The additional score may be computed based on the number of times of recommendations of the API/component by the user and the total number of times the user selected API/component is used. The additional score may be calculated using the following expression:
Additional Score=(Number of times of recommendation/number of times of usage)

The additional score may be calculated for the parameter whose combined score is more than system selected API/component. If the combined score is more in more than one parameter for the user selected API/component, the additional score is added for both parameter. For example, a user selects the D2 API/component even though system suggested the D1 API/component based on combined score. In D2 API/component, the requirements match score (6) is more than that of D1 API/component (5). So, the number of recommendation will be increased from previous recommendation value by 1. Now, assuming that Number of usage of API=13

The number of recommendation=2

This additional score is added for API score calculation, next/subsequent time.

If calculated requirements match score is 6

FIG. 5is a table of exemplary component scores and combined scores calculated for APIs or components selected in response to a user input request, in accordance with some embodiments of the present disclosure. Column510may list names of exemplary user input requirements and column520may list APIs or components selected by component selection module150in response to user requirements510. Further, columns530,540,550, and560may list the individual requirements matching score, functional and technical requirements matching score, performance score, and success rate score respectively. Column570may list combined score based on the individual scores and the corresponding weightage of the individual scores. As an example, for requirement Req-1 and selected API A1, requirements matching score530is computed to be zero and accordingly weightage of the functional and technical requirements matching score can be set at 0.7, weightage of the performance score can be set at 0.2 and weightage of the success rate score can be set at 0.1. Combined score570can therefore be calculated to be 5 using the expression “0+(6×0.7)+(4×0.2)+0.”

Referring back toFIG. 2, after the score calculation for components in step220, method200can proceed to filtering components in step225. If the calculated combined score for an API or component in step220is above a predetermined threshold value, component selection module150can provide it to component recommender120.

In step230, component recommender120can suggest the selected component provided by component selection module150to the user for developing the software application. Suggested components may be based on the scores calculated by component scoring module170in step220. For example, based on values listed in column570of table500, suggested components can be A2 (5.65) for Req-1, B1 (7.6) for Req-2, C1 (7.6) for Req-3, and D1 (3.9) for Req-4.

In some embodiments, component recommender120can determine if there are any solutions already built in and using a large proportion of the suggested components (for example, larger than a threshold proportion of 50%). The threshold proportion may be configurable by the user. Any solutions meeting the criteria can be provided to the user for consideration. The number of recommendations can be restricted by overall performance and threshold number of solutions. Further, overall performance of the solutions cannot be considered if none or only a few APIs or components of the solution are utilized in a sequence, leaving some APIs or components not utilized. Overall performance of the solutions can be considered if all APIs or components are involved in a much larger sequence.

In step235, component assembler140can detect and process if the user makes any changes to suggestions provided in step230. If any changes are detected and processed, the associated data can be stored in component metadata repository180and component data model160can be updated accordingly. Further, the API or component chosen by the user can be rewarded for matching user requirements by increasing the corresponding requirements matching score. This may enable the API or component to be suggested for similar future user requirements.

In an embodiment, if the API/component may be selected by a user which is not selected by the component selection module150, then the component scoring module170may calculate an additional score for that API/component. The additional score may be computed based on the number of times of recommendations of the API/component by the user and the total number of times the user selected API/component is used. The additional score may be calculated for the parameter whose combined score is more than system selected API/component. If the combined score is more in more than one parameter for the user selected API/component, the additional score is added for both parameter.

In step240, user interface110can determine if user has provided a dataset along with the input requirements. If no dataset is received from the user, method200can proceed to step250and output the complete solution. Some data analysis solutions cannot be completed without data modeling which is used as machine learning. Along with APIs and components, a data model built using preferred configuration may also be required.

In step245, a data model can be created based on the user input data set and type of data model required (for example, clustering, classification, regression and dimensionality reduction) and parameters such as optimization, LF, regularization, Activation Function). After data model is built, method200can proceed to step250and output the complete solution.

Computer System

FIG. 6is a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. Variations of computer system601may be used for implementing the devices and systems disclosed herein. Computer system601may comprise a central processing unit (“CPU” or “processor”)602. Processor602may comprise at least one data processor for executing program components for executing user- or system-generated requests. A user may include a person, a person using a device such as those included in this disclosure, or such a device itself. The processor may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM's application, embedded or secure processors, IBM PowerPC, Intel's Core, Itanium, Xeon, Celeron or other line of processors, etc. The processor602may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), Field Programmable Gate Arrays (FPGAs), etc.

Using the I/O interface603, the computer system601may communicate with one or more I/O devices. For example, the input device604may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, sensor (e.g., accelerometer, light sensor, GPS, gyroscope, proximity sensor, or the like), stylus, scanner, storage device, transceiver, video device/source, visors, etc. Output device605may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, or the like), audio speaker, etc. In some embodiments, a transceiver606may be disposed in connection with the processor602. The transceiver may facilitate various types of wireless transmission or reception. For example, the transceiver may include an antenna operatively connected to a transceiver chip (e.g., Texas Instruments WiLink WL1283, Broadcom BCM4750IUB8, Infineon Technologies X-Gold 618-PMB9800, or the like), providing IEEE 802.11a/b/g/n, Bluetooth, FM, global positioning system (GPS), 2G/3G HSDPA/HSUPA communications, etc.

In some embodiments, the processor602may be disposed in communication with a communication network608via a network interface607. The network interface607may communicate with the communication network608. The network interface may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network608may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface607and the communication network608, the computer system601may communicate with devices610,611, and612. These devices may include, without limitation, personal computer(s), server(s), fax machines, printers, scanners, various mobile devices such as cellular telephones, smartphones (e.g., Apple iPhone, Blackberry, Android-based phones, etc.), tablet computers, eBook readers (Amazon Kindle, Nook, etc.), laptop computers, notebooks, gaming consoles (Microsoft Xbox, Nintendo DS, Sony PlayStation, etc.), or the like. In some embodiments, the computer system601may itself embody one or more of these devices.