In software engineering software defect class prediction can help to take decision for proper allocation of resources in software testing phase. Identification of highly defect prone classes will get more attention from tester as well as security experts. In recent years various artificial techniques are used by researchers in different phases of SDLC. Main objective of the study is to compare the performances of Hybrid Search Based Algorithms in prediction of defect proneness of a class in software. Statistical test are used to compare the performances of developed prediction models, Validation of the models is performed with the different releases of datasets.

Regression testing is used to check that changes in the some functionality of the software to not affect its old behaviours. Test case prioritization is essential for reducing the cost of regression testing. In this paper a test cases prioritization model based on fuzzy logic is presented. State machine diagram is used to capture the behaviour of the system. Risk information is associated with the states. After change in the functionality of the system new state machine diagram is designed. This new state machine diagram is converted into Weighted Extended Finite State Machine (WEFSM). Weights are assigned to nodes and edges based on change and risk exposure. Risk exposure and change information of each test case is used as input to fuzzy model. Test cases are categorized in retestable, reusable and obsolete.