Application of Artificial Intelligence in Defect Tracking
- Date: 05 Aug, 2020
For many software development companies, the process of identifying and mitigating the defects and bugs in the application system can be tedious and difficult, especially if they are working on large-scale, complex projects. The increasing pressure of reducing the time to market without making a compromise on the quality of the end product puts further strain on the project teams. It is not uncommon for the teams to crack under this pressure and skip on some crucial steps of quality assurance to meet the deadlines. Missing the defects and errors in the early stages of the development increases the amount of time and cost invested in resolving the issues, and can also wreak havoc on the reputation of the company. Additionally, it can also have a severely adverse effect on the sustainability of the application on the choice list of the users. It is next to impossible to prevent defects and bugs from occurring in the project altogether, but what if you could predict when and where these defects would happen beforehand?
Application of Artificial Intelligence in Defect Tracking System
In an effort to increase the testing efficiency and to reduce the period between release cycles, many organizations are incorporating aspects and techniques of artificial intelligence into the defect tracking software. Powered by intelligent and robust techniques of artificial intelligence like machine learning, the quality assurance and the testing processes are improved while reducing the testing efforts of the team. As the abilities of the defect tracking system are augmented by the sophisticated techniques and methodologies of artificial intelligence, the project managers and teams are able to identify gaps in the quality of the application and the defect targets with the help of predictive analysis. Given that the AI based engine produces actionable insights for the improvement of development and testing cycle, they go a long way in enhancing the effectiveness and the efficiency of the testing processes. The redundancy and ineptitude of the quality assurance procedure is also minimized with the use of real time and offline analysis of the test cases, that are accessible to all relevant team members.
The basic aim of the defect prediction model in software testing which is powered by the AI engines and defect tracking software, is to basically reduce the testing efforts and testing costs of the project by guiding the team through the most defect prone components of the software application system. In the rapidly evolving testing landscape, defect predictors are becoming more common amongst the software development companies. As opposed to manual testing processes like code reviews, the AI based defect tracking system is primarily used to save time and efforts through a wide array of techniques and approaches. The fixing process of the software defects gets costlier and time consuming as the defects move through the development processes. Therefore, it is imperative for the managers and teams to catch them and in the best case scenarios predict them beforehand. This again emphasizes the importance of the AI based defect prediction models and frameworks, as they use different validation, verification, and testing strategies to optimize the time, efforts, and costs utilized in the entire process.
How Does It Work?
In a real life setting, the successful implementation of AI based models within the defect tracking system requires historical data from previous projects and software metrics to predict the defect prone areas of the new projects. A learning based AI model is created which combines both factors, software metrics and historical data, and uses it along with all pertinent information about the defects to train data sets. Based on the insights which are gathered from the training data sets and the software metrics which are acquired from the most recent project of the organization, the test managers are able to predict and estimate the defect proneness of the current projects. Research on the software data prediction models denotes that the artificial intelligence and machine learning frameworks merged with the defect tracking system are able to detect around 70 percent of the defects in the application system, on average. The main reason behind the efficacy and the popularity of these AI based models is the fact that they are similar to the working principle of the human brain. Akin to the human brain, AI based prediction models are able to collect previous knowledge from various sources on a given subject, analyze and assess the information, learn from the previous mistakes and errors, and then provide near accurate predictions and estimates.
