Abstract: A system for predicting performance failures in a computer program during the course of its development is provided. The system comprises a pattern identifier module that identifies frequent sets of source code files that have been modified together in plurality of historical builds. The system further comprises a performance failure extraction module that identifies frequent sets of performance failures that have been logged together in the plurality of historical builds. The system furthermore comprises a correlation engine that establishes patterns between the frequent sets of modified source code files and the frequent sets of performance failures by identifying co-occurrence between the frequent sets of modified source code files and the frequent sets of performance failures for each historical build.

Abstract: A system for predicting defects in a computer program during its development is provided. The system comprises a pattern identifier module which identifies frequent sets of source code files that have been modified together in plurality of historical builds. The system further comprises a keyword extractor module which identifies frequent sets of defect keywords that have been logged together in the plurality of historical builds. The system furthermore comprises a correlation engine which establishes patterns between the frequent sets of modified source code files and the frequent set of defect keywords by identifying co-occurrence between the frequent sets of modified source code files and the frequent sets of defect keywords for each historical build. Finally, the system further comprises a defect recommendation engine which predicts defects in new builds associated with the computer program based on the established patterns.

Abstract: A system and a method for optimizing software quality assurance during various phases of Software Development Process (SDP) is provided. In particular, the present invention provides for generating machine learning (ML) models corresponding to respective phases of the SDP based on historical data. Further, each of the generated ML models associated with respective phases of the SDP are configured with a set of parameters. Furthermore, a model configuration corresponding to each phase of SDP is identified by executing configured models on the historical data and a set of predefined result-parameters is analyzed. Yet further, quality assurance events are optimized by analyzing real-time data associated with respective phases of SDP using the identified model configuration corresponding to respective phases.

Abstract: A system for predicting performance failures in a computer program during the course of its development is provided. The system comprises a pattern identifier module that identifies frequent sets of source code files that have been modified together in plurality of historical builds. The system further comprises a performance failure extraction module that identifies frequent sets of performance failures that have been logged together in the plurality of historical builds. The system furthermore comprises a correlation engine that establishes patterns between the frequent sets of modified source code files and the frequent sets of performance failures by identifying co-occurrence between the frequent sets of modified source code files and the frequent sets of performance failures for each historical build.

Abstract: A system for predicting defects in a computer program during its development is provided. The system comprises a pattern identifier module which identifies frequent sets of source code files that have been modified together in plurality of historical builds. The system further comprises a keyword extractor module which identifies frequent sets of defect keywords that have been logged together in the plurality of historical builds. The system furthermore comprises a correlation engine which establishes patterns between the frequent sets of modified source code files and the frequent set of defect keywords by identifying co-occurrence between the frequent sets of modified source code files and the frequent sets of defect keywords for each historical build. Finally, the system further comprises a defect recommendation engine which predicts defects in new builds associated with the computer program based on the established patterns.