Augmenting the Benefits of Issue Tracking with Machine Learning
A software application continuously develops and evolves over time. As the application is developed and maintained over time, the bugs and defects are filled and assigned to the resources so that they can be resolved. Depending on the nature of the software application and the scope of the project, manual or automated issue tracking tools can be used. After the bugs are detected in the system and reported by the testing team, it is of utmost importance to analyze their attributes of the issues and then subsequently allocate them to various members for management and resolution.
Communication about the requirements and needs of the software development lifecycle is generally conducted through the issue tracking system as well, especially in case of large scale projects where team members are required to multi-task. The most significant component of this software is that its documentation which comprises all factual and historical information of the issues which are applicable in many situations. Issue tracking tools are essentially used by project managers and teams to log in defects, errors, and complaints from multiple sources on one platform so that they can be monitored easily. All the reported issues are then classified, triaged, and structure properly according to their severity and priority for better visibility and traceability.
Role of Machine Learning in Issue Tracking System
The techniques and methodologies of artificial intelligence and machine learning are no longer a novel phenomenon, lieu they are widely accepted and adopted in the software testing companies, given their inclination towards agile and DevOps environment. There are many ways in which the sophisticated and advanced techniques of machine learning can reduce the testing costs and invested time, while maintaining quality and optimizing testing efforts. From the early detection of bugs in the system to opening new avenues for keeping at par with the software quality standards, machine learning has a significant bearing on the issue tracking and management of the software applications.
Over the past few years, the task of recording, managing, and then fixing the detected bugs has become a challenge for the teams This is because, in order to stay ahead in the game, companies are constantly introducing multiple dimensions and elements in the application; subsequently, the amount of work to be completed in the same time increases substantially. Assessment and analysis of the increased number of issues within the bounds of time and limited resources is a daunting task in of itself, not to mention the need to assign adequate severity levels, prioritize and organize issues, and allocating them to the right team members. Hence, machine learning-based models are integrated along with an issue tracking system to not only detect bugs in the early phases, but to also identify defect prone areas in the application. By developing fault prediction models with the use of machine learning and embedding them in the development cycle and quality assurance processes, the maintenance costs of the companies have reduced by a significant margin.
The central repository or the database which records all data pertaining to issues is mainly used for the development of machine learning-based prediction models. Using the evolutionary data of bugs from the database, essential software metrics and historical data is created, which is in turn used for training data sets and predicting defects before they happen. With the effective use of machine learning techniques, defects patterns and trends are not only extracted but they are also analyzed extensively to highlight the most vulnerable features and modules. Many different types of software metrics including, code, design, and requirements can be used for the accurate prediction of issues and bugs in the application.
Defects and issues are part and parcel of the software development cycle, while some of them have high levels of severity, there are others that have little to no impact on the project outcome. This is where the advanced methodologies of machine learning and issue tracking software come in handy, as they save a lot of time, effort, and cost by determining the severity and priority of issues easily. As the machine learning models incorporated in issue tracking system allows the teams to predict defects in the system, the team members can then work towards highlighting areas which have the most complex and severe issue and focus their testing efforts primarily towards those areas.