EXPERTISE SCORE VECTOR BASED WORK ITEM ASSIGNMENT FOR SOFTWARE COMPONENT MANAGEMENT

Techniques for expertise score vector based work item assignment for software component management are described herein. An aspect includes receiving a problem record corresponding to a software component. Another aspect includes determining a work item corresponding to the problem record. Another aspect includes assigning the work item to a developer based on an expertise score vector of the developer. Another aspect includes, based on completion of the work item by the developer, updating the expertise score vector of the developer

BACKGROUND

The present invention generally relates to computer systems, and more specifically, to expertise score vector based work item assignment for software component management in a computer system.

Computer systems control almost every aspect of our life—from writing documents to controlling traffic lights. Such computer systems are controlled by software components that may be written by teams of software developers. The software components may be relatively complex, requiring relatively large numbers of developers working together to produce and maintain computer code that is executed on a computer system. Further, computer systems may be often error-prone, and thus require a testing phase in which any errors should be discovered. The testing phase is considered one of the most difficult tasks in designing a computer system. The cost of not discovering an error may be enormous, as the consequences of the error may be disastrous.

SUMMARY

Embodiments of the present invention are directed to expertise score vector based work item assignment for software component management. A non-limiting example computer-implemented method includes receiving a problem record corresponding to a software component. The method also includes determining a work item corresponding to the problem record. The method also includes assigning the work item to a developer based on an expertise score vector of the developer. The method also includes, based on completion of the work item by the developer, updating the expertise score vector of the developer

DETAILED DESCRIPTION

One or more embodiments of the present invention provide expertise score vector based work item assignment for software component management. An organization may produce and maintain computer software products for use on computer systems that include multiple software components. Each software component may be assigned a team of developers that are responsible for the software component. Creating software (i.e., developing) for different computer systems that implement relatively complex software components may require specialized knowledge and skills by a software developer. Such knowledge and skills may be gained through experience developing for a particular computer system and/or software component. In order to maintain relatively high quality in software that is produced by an organization, respective expertise score vectors may be maintained for each developer in an organization to identify levels of skills and component mastery for individual developers. Work items may be assigned to developers based on expertise scores that are determined based on the expertise score vectors. For example, a more experienced developer having a higher expertise score may be assigned relatively complex work items, while a less experienced developer having a lower expertise score may be assigned relatively simple work items.

Problem records may be generated based on detection of a problem with a software component. A work item may be generated based on a problem record, and the work item may be assigned to a developer so that the developer may fix the problem. Assignment of a work item corresponding to a problem record may be performed based on the expertise score vectors that are maintained for developers on a team corresponding to the software component. Usage of the expertise score vector to assign work items may ensure that problems with the software component are handled by one or more developers having appropriate skillsets and experience levels. When a work item is correctly assigned, the underlying problem may have a relatively short resolution time and a higher quality solution, due to the assigned developer's expertise.

An expertise score vector corresponding to a developer may include any appropriate developer metrics, including but not limited to component mastery metrics for any software components the developer has worked on, a language set (e.g., Java, Python, C, etc.), coding techniques, code patterns, and quality metrics regarding completed work (i.e., code contributions) by the developer. Completion of a unit of contribution by a developer to a software component may be determined based on, for example, committing of code to a code base corresponding to the software component. When a developer contributes to a software component, information regarding the code contribution may be identified, such as the programming language of the code and/or any programming techniques used in the code. The expertise score vector of a developer may track a number of commits by the developer per skill (e.g., programming language). A code contribution may be scanned (using, for example, static code analysis and/or natural language processing) to identify what the code does and any techniques that are implemented in the code contribution. For example, a developer may often write code involving particular techniques or technologies, including but not limited to recursion, loops, thread management, mutex locks, and interfacing with certain subcomponents.

Information about the type of work each developer in a team has completed may be tracked each developer's respective expertise score vector, providing a corpus of team member skills. Team member skills may be stored and/or displayed in any appropriate manner (e.g., a word bubble and/or a database), and may be used for any appropriate task, such as determining work item assignments. When a new problem record is received for a software component, the contents of the problem record may be analyzed to determine information regarding the problem. The analysis may include natural language processing (NLP) in some embodiments, and keywords in the problem record may be identified by the analysis. The determined keywords may then be compared to the developer skills on the team that owns the software component, and any matches between developers on the team and the problem record may be identified. If no matches are found for a new problem record on a particular team, other teams in the organization may be scanned for appropriate skills to handle the problem record, or multiple developers having relatively closely related skills may be assigned to work on the problem record as a group. In some embodiments, an alert may be issued to a team lead regarding a lack of matches for the problem record.

In order to enable a team to train employees in a certain skill, when a problem record is received that is related to the certain skill, multiple developers from the team may be assigned to the problem record as a group. At least one assignee in the group may be an experienced developer that has skills that match the problem record, while other developers in the group may be assigned to learn. In some embodiments, an expertise score vector may track a count of the number of defects found in code related to different skills, which may be used to determine whether a trainee is learning new skills.

As shown inFIG. 1, the computer system100has one or more central processing units (CPU(s))101a,101b,101c,etc. (collectively or generically referred to as processor(s)101). The processors101can be a single-core processor, multi-core processor, computing cluster, or any number of other configurations. The processors101, also referred to as processing circuits, are coupled via a system bus102to a system memory103and various other components. The system memory103can include a read only memory (ROM)104and a random access memory (RAM)105. The ROM104is coupled to the system bus102and may include a basic input/output system (BIOS), which controls certain basic functions of the computer system100. The RAM is read-write memory coupled to the system bus102for use by the processors101. The system memory103provides temporary memory space for operations of said instructions during operation. The system memory103can include random access memory (RAM), read only memory, flash memory, or any other suitable memory systems.

The computer system100comprises an input/output (I/O) adapter106and a communications adapter107coupled to the system bus102. The I/O adapter106may be a small computer system interface (SCSI) adapter that communicates with a hard disk108and/or any other similar component. The I/O adapter106and the hard disk108are collectively referred to herein as a mass storage110.

Software111for execution on the computer system100may be stored in the mass storage110. The mass storage110is an example of a tangible storage medium readable by the processors101, where the software111is stored as instructions for execution by the processors101to cause the computer system100to operate, such as is described herein below with respect to the various Figures. Examples of computer program product and the execution of such instruction is discussed herein in more detail. The communications adapter107interconnects the system bus102with a network112, which may be an outside network, enabling the computer system100to communicate with other such systems. In one embodiment, a portion of the system memory103and the mass storage110collectively store an operating system, which may be any appropriate operating system, such as the z/OS or AIX operating system from IBM Corporation, to coordinate the functions of the various components shown inFIG. 1.

Additional input/output devices are shown as connected to the system bus102via a display adapter115and an interface adapter116and. In one embodiment, the adapters106,107,115, and116may be connected to one or more I/O buses that are connected to the system bus102via an intermediate bus bridge (not shown). A display119(e.g., a screen or a display monitor) is connected to the system bus102by a display adapter115, which may include a graphics controller to improve the performance of graphics intensive applications and a video controller. A keyboard121, a mouse122, a speaker123, etc. can be interconnected to the system bus102via the interface adapter116, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit. Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Thus, as configured inFIG. 1, the computer system100includes processing capability in the form of the processors101, and, storage capability including the system memory103and the mass storage110, input means such as the keyboard121and the mouse122, and output capability including the speaker123and the display119.

In some embodiments, the communications adapter107can transmit data using any suitable interface or protocol, such as the internet small computer system interface, among others. The network112may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, among others. An external computing device may connect to the computer system100through the network112. In some examples, an external computing device may be an external webserver or a cloud computing node.

It is to be understood that the block diagram ofFIG. 1is not intended to indicate that the computer system100is to include all of the components shown inFIG. 1. Rather, the computer system100can include any appropriate fewer or additional components not illustrated inFIG. 1(e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). Further, the embodiments described herein with respect to computer system100may be implemented with any appropriate logic, wherein the logic, as referred to herein, can include any suitable hardware (e.g., a processor, an embedded controller, or an application specific integrated circuit, among others), software (e.g., an application, among others), firmware, or any suitable combination of hardware, software, and firmware, in various embodiments.

Turning now toFIG. 2, a process flow diagram of a method200for expertise score vector based work item assignment for software component management is generally shown in accordance with one or more embodiments of the present invention. Method200may be implemented in conjunction with any appropriate computer system, such as computer system100ofFIG. 1. In block201of method200, an incoming problem record is received and processed to determine a corresponding work item. The problem record may have been generated based on an underlying problem (e.g., bug) in a software component. The processing may include natural language processing (NLP) of the problem record to determine any keywords from the problem record to be included in the work item. The keywords may be any appropriate descriptor of the underlying problem corresponding to the problem record, including but not limited to a programming language, programming techniques, and/or descriptors of a computing environment in which the problem occurred. A minimum expertise score for the work item may also be determined in block201. The minimum expertise score may be determined based on the severity and complexity of the underlying problem. The minimum expertise score may also be determined based on an amount of time the deployed code corresponding to the problem report has been in the field. For a problem report corresponding to deployed code that has been in the field a relatively long amount of time, a higher minimum expertise score may be required, as compared to a problem report corresponding to deployed code that has been in the field a relatively short amount of time. The work item may also be assigned a priority in block201based on a desired timeframe for completion of the work item. For example, for a work item corresponding to a major loss of functionality in a deployed software component, a high priority may be assigned. In block202, the work item enters a work item queue corresponding to the software component. The position of the work item in the work item queue may be determined based on the priority of the work item.

In block203, a developer from the team corresponding to the software component is selected for the work item based on an expertise score vector of the developer. The developer may be selected in block203based on any appropriate information from the expertise score vector, such as an overall component mastery metric and a skillset of the developer. An expertise score may be determined for the developer in block203based on a subset of fields relevant to the work item in the expertise score vector (e.g., metrics directly related to a software component and/or skill relevant to the work item); a magnitude of a vector including the subset of fields may be used to determine the expertise score. The expertise scores of all developers on a team may be compared to a minimum expertise score that was determined for the work item in block201, and a developer having an expertise score higher than the minimum expertise score may be selected in block203. An embodiment of an expertise score vector is discussed in further detail below with respect toFIG. 4B. Information about the type of work each developer in a team has completed may be tracked using each developer's respective expertise score vector, providing a corpus of team member skills that may be used to select a developer for the work item in block203. Team member skills may be stored and/or displayed in any appropriate manner (e.g., a word bubble and/or a database). In some embodiments of block203, keywords that were determined by NLP in block201may be compared to the developer skills on the team that owns the software component corresponding to the work item, and any matches between developers on the team and the work item may be identified. If no matches are found for a new work item on a particular team in block203, other teams in the organization may be scanned for appropriate skills to handle the work item, or multiple developers having relatively closely related skills may be assigned to work on the work item as a group. In some embodiments, an alert may be issued to a team lead regarding a lack of matches for the work item. In some embodiments, the selected developer may refuse the assignment of the work item in block203; in such embodiments, another developer having skills that are relatively close to the work item may be selected. In some embodiments, multiple developers may be assigned to a single work item in block203based on their respective expertise scores.

The work item may also be assigned in block203based on each developer's currently assigned workload, which may be quantified by a work queue points value. A work item management module may track any in-progress work items that are currently assigned to each developer on a team. For two developers on a team that match a new work item, the new work item may be assigned to the developer having a smaller work queue and corresponding lower number of work queue points. Work queue points may be determined for a developer based on the developer's currently assigned workload. The work queue points may be used to determine respective workloads of developers on a team, and work items may be assigned to specific developers on the team based on the developer's respective work queue points in block203.

In block204, a review and testing process for the work item is determined based on the expertise score vector(s) of the assigned developer(s). For example, if a developer that was selected in block203is a less experienced developer, or has a relatively low overall component mastery metric for a software component or skill corresponding to the work item, a more rigorous code review and testing process (including, for example, additional review or testing iterations, and/or more experienced reviewers or testers) may be determined to be necessary for the work item. For a developer having higher component mastery or skill metrics, a less rigorous code review and testing process may be determined. In block205, based on the developer completing the work item, the review and testing process that was determined in block204is applied to the code that the developer submitted corresponding to the work item. In block206, the developer's expertise score vector is updated based on completion of review and testing of the code corresponding to the work item. For example, in block206, the developer's component mastery metrics for the software component corresponding to the work item may be updated to a value corresponding to a higher developer tier based on relatively fast completion of relatively high quality code corresponding to the work item, and/or additional skills may be added to the developer's skillset metrics in the expertise score vector. For any errors detected in the committed code, one or more metrics in the developer's expertise score vector may be decreased in block206. Embodiments of method200may be implemented in software component management system400ofFIGS. 4A-B, which is discussed in further detail below.

The process flow diagram ofFIG. 2is not intended to indicate that the operations of the method200are to be executed in any particular order, or that all of the operations of the method200are to be included in every case. Additionally, the method200can include any suitable number of additional operations.

FIG. 3illustrates a process flow diagram of a method300for developer training using expertise score vector based work item assignment for software component management that is generally shown in accordance with one or more embodiments of the present invention. Method300may be implemented in conjunction with any appropriate computer system, such as computer system100ofFIG. 1. In block301ofFIG. 3, a work item is determined based on processing of an incoming problem record, and is entered into a work item queue corresponding to a software component. Block301may be performed as described above with respect to blocks201and202ofFIG. 2.

In block302, it is determined that the work item is related to a desired skill. For example, a manager may desire that developers on a particular team gain knowledge regarding a particular programming language, technique, and/or computing environment. The determination of block302may be made based on any keywords that were determined by the processing of the problem record in block301. In block303, an experienced developer having skills that match the work item, including the desired skill, is selected based on the experienced developer's expertise score vector. The selection of block303may be performed as described above with respect to block203.

In block304, one or more additional developers on the team that do not have the desired skill are selected and assigned to work on the work item with the experienced developer that was selected in block303. The determination of any additional developers that do not have the desired skill may be made based on skills that are not included the developer's respective expertise score vectors, or based on the desired skill indicating a relatively low level of mastery in the developer's respective expertise score vectors.

In block305, based on completion of the work item (e.g., code is committed into the software component code base corresponding to the work item), the expertise score vectors of the one or more additional developers are updated. Committed code corresponding to the work item may be reviewed and tested in block305to determine code quality. An amount of time in development for the committed code may also be determined, and a number of units of contribution (e.g., number of lines of code) may be determined to update component mastery metrics in the expertise score vector in block305. In some embodiments, the expertise score vector may track a count of a number of defects found in code related to the desired skill, which may be used to determine whether a trainee developer is progressing in learning the desired skill.

The process flow diagram ofFIG. 3is not intended to indicate that the operations of the method300are to be executed in any particular order, or that all of the operations of the method300are to be included in every case. Additionally, the method300can include any suitable number of additional operations.

Turning now toFIG. 4A, a software component management system400that includes an expertise score vector is generally shown in accordance with one or more embodiments of the present invention. Software component management system400may be implemented in conjunction with any appropriate computer system(s), including but not limited to computer system100ofFIG. 1. Software component management system400is in communication with software component code bases410A-N, which each include computer code written by one or more developers on teams corresponding to various software components. The software component management system400includes an expertise score vector module401, which may maintain a respective expertise score vector of expertise score vectors402A-N for each developer across various teams in the organization. Expertise score vector module401and expertise score vectors402A-N are discussed in further detail below with respect toFIG. 4B.

Software component management system400includes a problem records module403, which receives and manages problem records (e.g., bug reports) regarding the software component code bases410A-N. NLP module404performs analysis of problem records that are received by problem records module403and may, for example, output keywords that are identified in a problem record to work item management module405. Work item management module405creates work items based on problem records that are received by problem records module403. The work items may be created by work item management module405based on keywords that were identified by NLP module404in some embodiments. Work item management module405may also create work items based on new feature requests for the software components corresponding to software component code bases410A-N. Created work items are placed in a work item queue406by work item management module405. The work items in work item queue406are assigned to developers by work item management module405based on input from expertise score vector module401and data from the developers' respective expertise score vectors402A-N. Work queue points module440may track a respective workload for each developer that is currently assigned to any work items in work item queue406.

When new code is committed by a developer into any of software component code bases410A-N, code analysis module407may review the new code to determine a code quality of the new code. Review and testing module408may determine and apply a review and testing process to new code, and may also assign one or more developers to the review and testing process based on expertise score vectors402A-N. Review and testing module408may also provide data regarding the review and testing of code to expertise score vector module401.

Component complexity and onboarding score module409may determine a relative component complexity and an onboarding score for each software component corresponding to software component code bases410A-N. Component complexity and onboarding score module409may operate based on component mastery metrics431A-N and developer classification module422ofFIG. 4B, which are discussed below.

Software component management system400may implement embodiments of method200ofFIG. 2. For example, an incoming problem record may be received by problem records module403and processed by NLP module404to determine keywords in the problem record in block201. A work item may be created by work item management module405and entered into work item queue406in block202. A developer may be chosen for the work item by expertise score vector module401based on the developer's associated expertise score vector402N in block203. In block204, a review and testing process may be determined for the work item by review and testing module408, and the determined review and testing process may be applied to completed code corresponding to the work item by review and testing module408in block205. In block206, the developer's expertise score vector402N may be updated by expertise score vector module401based on the review and testing of the completed code.

Software component management system400may implement embodiments of method300ofFIG. 3. For example, an incoming problem record may be received by problem records module403and processed by NLP module404to determine keywords in the problem record in block301. A work item may be created by work item management module405and entered into work item queue406in block302, and work item management module405may determine that the work item relates to a desired skill. An experienced developer may be chosen for the work item based on data from expertise score vector module401and the developer's associated expertise score vector402N in block303. In blocks304, an additional developer that does not have the desired skill may be selected based on the additional developer's associated expertise score vector402A. In block305, the additional developer's expertise score vector402A may be updated by expertise score vector module401based on the review and testing of the completed code corresponding to the work item.

It is to be understood that the block diagram ofFIG. 4Ais not intended to indicate that the system400is to include all of the components shown inFIG. 4A. Rather, the system400can include any appropriate fewer or additional components not illustrated inFIG. 4A(e.g., additional memory components, embedded controllers, functional blocks, connections between functional blocks, modules, inputs, outputs, etc.). Further, the embodiments described herein with respect to system400may be implemented with any appropriate logic, wherein the logic, as referred to herein, can include any suitable hardware (e.g., a processor, an embedded controller, or an application specific integrated circuit, among others), software (e.g., an application, among others), firmware, or any suitable combination of hardware, software, and firmware, in various embodiments.

Turning now toFIG. 4B, an expertise score vector module401is generally shown in accordance with one or more embodiments of the present invention. Expertise score vector module401ofFIG. 4Bcorresponds to expertise score vector module401ofFIG. 4A, and manages a plurality of expertise score vectors402A-N. Expertise score vector module401includes an expertise score vector update module420, which may update any field in an expertise score vector402N based on data from problem records module403, work item management module405, code analysis module407, and review and testing module408in software component management system400.

Expertise score calculation module421may determine an expertise score for a developer based on the developer's expertise score vector402N. An expertise score may be determined based on any appropriate subset of the fields in expertise score vector402N, and the various fields in expertise score vector402N may each be given any appropriate weight in calculating an expertise score. An expertise score may be calculated by expertise score calculation module421for a specific skill in some embodiments, such that only fields related to the specific skill are used to calculate the expertise score for the specific skill. In some embodiments, an expertise score that is calculated for a specific skill or software component may be used to assign work items to developers by work item management module405as described in method200ofFIG. 2and method300ofFIG. 3. Developer classification module422may determine a classification for a developer based on an expertise score from expertise score calculation module421. In some embodiments, the developer classification that is calculated by developer classification module422may be used to assign work items to developers as described in method200ofFIG. 2and method300ofFIG. 3.

Expertise score vector402N corresponds to a single developer in an organization. Expertise score vector402N includes a developer and team identifier430, which includes a unique identifier of the developer corresponding to expertise score vector402N, and any teams that the developer is part of. A developer may be part of multiple teams in some embodiments. Expertise score vector402N includes a plurality of data fields corresponding to the developer.

Expertise score vector402N may include respective component mastery metrics431A-N for each software component that the developer has contributed work to. Component mastery metrics431A-N may include an amount of time required by the developer to produce a unit of contribution to the associated software component. The unit of contribution may be measured in any appropriate manner (e.g. task completed, or lines of code). A number of errors or defects found in committed code by, for example, code analysis module407and/or review and testing module408, that is related to a specific software component may also be tracked. For example, a number of defects detected in code per unit of contribution (e.g., lines of code or number of tasks) for a specific software component may be stored in component mastery metrics431A-N. The component mastery metrics431A-N may also include an amount of time spent on the software component, and a total number of contributions made to the software component. Developer classification module422may classify the developer with respect to a specific software component based on a set of component mastery metrics431A, or an overall component mastery metric corresponding to the specific software component. Work items may be assigned to the developer based on the classifications determined by developer classification module422, and also based on the developer's work queue points from work queue points module440.

Expertise score vector402N may include a plurality of developer skill metrics432A-N. Each individual set of developer skill metrics432A-N may correspond to a specific skill (e.g., a programming language, a programming technique, such as recursion or multithreading, or a specific hardware element) possessed by the developer. Any appropriate metrics, including skill level and time spent on the skill, may be maintained in the developer skill metrics, such as developer skill metrics432A, corresponding to a specific skill. Developer skill metrics432A-N may be used in block203of method200ofFIG. 2, and blocks303and304of method300ofFIG. 3, to select developers to assign to a particular work item. The developer skill metrics432A-N may include any appropriate metrics, including but not limited to a language set (e.g., Java, Python, C, etc.), coding techniques, and code patterns. Developer skill metrics432A-N may track any appropriate particular techniques or technologies, including but not limited to recursion, loops, thread management, mutex locks, and interfacing with specific subcomponents. The developer skill metrics432A-N may track a number of commits by the developer per skill to quantify an amount of experience the developer has regarding the skill. Errors in code committed that is related to the skill may also be tracked. A number of errors or defects found in committed code by, for example, code analysis module407and/or review and testing module408, that are related to the skill may also be tracked. For example, a number of defects detected in code per unit of contribution (e.g., lines of code or number of tasks) for a specific skill may be stored in developer skill metrics432A-N. A code contribution by the developer may be scanned by code analysis module407(using, for example, static code analysis and/or NLP) to identify what the code does and any techniques that are implemented in the code contribution, and the developer skill metrics432A-N may be updated based on the scanning. Expertise score vector402N may also include code quality metrics433, problem records metrics434, regression testing metrics435, and code review change metrics436.

It is to be understood that the block diagram ofFIG. 4Bis not intended to indicate that the expertise score vector module401is to include all of the components shown inFIG. 4B. Rather, the expertise score vector module401can include any appropriate fewer or additional components not illustrated inFIG. 4B(e.g., additional memory components, embedded controllers, functional blocks, connections between functional blocks, modules, inputs, outputs, etc.). Further, the embodiments described herein with respect to expertise score vector module401may be implemented with any appropriate logic, wherein the logic, as referred to herein, can include any suitable hardware (e.g., a processor, an embedded controller, or an application specific integrated circuit, among others), software (e.g., an application, among others), firmware, or any suitable combination of hardware, software, and firmware, in various embodiments. Further, expertise score vector402N is shown for illustrative purposes only. Embodiments of an expertise score vector such as expertise score vector402N may include any appropriate number and type of data fields in various embodiments.