Many software-related failures or faults were caused as the consequence of not detecting it early and prevailing constraints of time and supplies available during any software examination. Having identified the challenges in the regression software testing of any software, many have started moving their attention towards the test cases or else validation suites prioritization. In this work, we have taken the Firefox bugs Report as the input Dataset comprising 12,486 test cases in it. Dealing with these kind of greater number test cases might consume our times and resources to the core for which the work is proposed. The dataset will be subjected to pre-processing operation to remove the unwanted contents in the bug reports like the assertions in some cases. Necessary features are then selected by using the algorithm called PCA, and attributes for clustering and prioritization will be determined by using the Dimensionality reduction. After the feature selection process, we make use of the agglomerative K–means clustering algorithm which helps to form clustered groups. After clustering process, we apply the ranking algorithm and prioritized the test cases in the clusters by using it. Finally, the performance of this work was analyzed and cross-verified by analyzing the priority ranking for clusters, sum of the distinct faults detected based on the number of test cases, adjacency matrix between test cases and fault detection rate.

由于未及早发现而导致许多与软件相关的故障或错误，并且在任何软件检查期间都存在时间和可用备件的普遍限制。在确定了任何软件的回归软件测试中的挑战之后，许多人开始将注意力转移到测试用例或验证套件的优先级上。在这项工作中，我们将Firefox错误报告作为输入数据集，其中包含12,486个测试用例。处理这类数量更多的测试用例可能会将我们的时间和资源浪费在拟议工作的核心上。数据集将进行预处理操作，以删除错误报告中不需要的内容，例如在某些情况下的断言。然后使用称为PCA的算法选择必要的功能，聚类和优先级划分的属性将通过降维来确定。在特征选择过程之后，我们利用聚集的K均值聚类算法，该算法有助于形成聚类的组。在聚类过程之后，我们应用排名算法，并使用该算法对聚类中的测试用例进行优先级排序。最后，通过分析群集的优先级排序，基于测试用例的数量检测到的不同故障的总和，测试用例之间的邻接矩阵和故障检测率，对这项工作的性能进行了分析和交叉验证。在聚类过程之后，我们应用排名算法，并使用该算法对聚类中的测试用例进行优先级排序。最后，通过分析群集的优先级排序，基于测试用例的数量检测到的不同故障的总和，测试用例之间的邻接矩阵和故障检测率，对这项工作的性能进行了分析和交叉验证。在聚类过程之后，我们应用排名算法，并使用该算法对聚类中的测试用例进行优先级排序。最后，通过分析群集的优先级排序，基于测试用例的数量检测到的不同故障的总和，测试用例之间的邻接矩阵和故障检测率，对这项工作的性能进行了分析和交叉验证。