Abstract: A system and a method for creating an optimal test suite. The system may receive code coverage data from a set of testing channels. Further, a combined dataset is created by merging the code coverage data received from the set of testing channels. The combined dataset is analyzed to identify a line of code being executed by two or more test cases corresponding to two or more testing channels of the set of testing channels. Further, at least one test case from the two or more test cases having less efficiency is eliminated to create an optimal test suite. The efficiency is determined based on at least one of execution time, execution cost, and resources required to execute the line of code.

Abstract: A system and a method for automatically testing software builds. The system includes testing a first software build using a test package. The test package includes at least a test strategy, a test case, a test model, an automation test script, a crowdsource script, and a manual test script. Further, baseline data is generated based upon a successful execution of the test package on the first software build. Further, a second software build is tested using the test package. Subsequently, the target data is generated based upon an execution of the test package on the second software build. The system then identifies a change in the second software build by comparing the target data with the baseline data. Further, a modification is recommended to the test package for the second software build using Artificial Intelligence (AI) techniques and Natural Language Processing (NLP).

Abstract: A system and a method for recommending a modification to a test package for a software under test. A release note package associated to a feature of a software is received. The release note package is analysed in real time using machine learning based models. Further, a keyword is extracted from the release note package using a keyword extraction technique. The keyword corresponds to the feature of the software. The keyword is compared with nomenclatures present in a test package using a pattern matching technique. The test package is associated to the feature of the software. Finally, a modification to the test package is recommended based on the comparison. The modification comprises addition, deletion, or updating an existing element of the test package. It may he noted that the modification is recommended using an Artificial Intelligence (AI) technique.

Abstract: A system and a method for classifying a test case executed on a software. Post execution, an actual result of the test case is received. A probability of the actual result being either a true failure or a false failure is determined. Further, the actual result is classified as the true failure or the false failure based on the probability. Subsequently, a recursive execution of the test case is recommended when the actual result is classified as the false failure until the actual result is classified as the true failure or a true pass. If the recursive execution fails to lead to either true positive or true negative, a reviewer' feedback is received for classification. Finally, a deviation between the classification and the feedback is recorded to classify results of subsequent test cases as true failures or false failures using an adaptive intelligence technique.