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Context: During the development process of a software program, regressiontesting is used to ensure that the correct behavior of the software is retainedafter updates to the source code. This regression testing becomes costly overtime as the number of test cases increases and it makes sense to prioritizetest cases in order to execute fault-detecting test cases as soon as possible.There are many coverage-based test case prioritization (TCP) methods that onlyuse the code coverage data to prioritize test cases. By incorporating thefault-proneness estimations of code units into the coverage-based TCP methods,we can improve such techniques.  Objective: In this paper, we aim to propose an approach which improvescoverage-based TCP methods by considering the fault-proneness distribution overcode units. Further, we present the results of an empirical study that showsusing our proposed approach significantly improves the additional strategy,which is a widely used coverage-based TCP method.  Method: The approach presented in this study uses the bug history of thesoftware in order to introduce a defect prediction method to learn a neuralnetwork model. This model is then used to estimate fault-proneness of each areaof the source code and then the estimations are incorporated intocoverage-based TCP methods. Our proposed approach is a general idea that can beapplied to many coverage-based methods, such as the additional and total TCPmethods.  Results: The proposed methods are evaluated on datasets collected from thedevelopment history of five real-world projects including 357 versions intotal. The experiments show that using an appropriate bug history can improvecoverage-based TCP methods.

Incorporating fault-proneness estimations into coverage-based test case prioritization methods