Abstract
Improvements and acceleration in software development has contributed towards high quality services in all domains and all fields of industry causing increasing demands for high quality software developments. In order to match with the high-quality software development demands, the software development industry is adopting human resources with high skills, advanced methodologies and technologies for accelerating the development life cycle. In the software development life cycle, one of the biggest challenges is the change management between versions of the source codes. The versing of the source code can be caused by various reasons such as change in the requirements or adaptation of functional update or technological upgradations. The change management does not only affect the correctness of the release for the software service, rather also impact the number of test cases. It is often observed that, the development life cycle is delayed due to lack of proper version control and due to the improver version control, the repetitive testing iterations. Hence the demand for better version control driven test case reduction methods cannot be ignored. A number of version control mechanisms are proposed by the parallel research attempts. Nevertheless, most of the version controls are criticized for not contributing towards the test case generation of reduction. Henceforth, this work proposes a novel probabilistic refactoring detection and rule-based test case reduction method in order to simplify the testing and version control mechanism for the software development. The refactoring process is highly adopted by the software developers for making efficient changes such as code structure, functionality or apply change in the requirements. This work demonstrates a very high accuracy for change detection and management. This results into a higher accuracy for test case reductions. The final outcome of this work is to reduce the development time for the software for making the software development industry a better and efficient world.
Highlights
The improvements in the code development is a must to be performed task for all software development cycles, due to the continuous changing client requirements
The effect of refactoring on the software source code is highly compatible with the change management process and further with the other phases of software development life cycle
Once the tokenization is completed, the same source code files are pushed to the proposed Prerequisite Requirement Change Detection (PRCD), proposed Code Feature Change Detection (CFCD) and proposed Source Functionality Change Detection (SFCD) algorithms
Summary
The improvements in the code development is a must to be performed task for all software development cycles, due to the continuous changing client requirements. The effect of refactoring on the software source code is highly compatible with the change management process and further with the other phases of software development life cycle. Understanding that the refactoring (Fig. 2) of the source code can be highly helpful for source code changing, most of the development practices uses this method. The rest of the work is furnished such as, the outcomes from the parallel researcher are analyzed, in Section III, problem definition and the scope for improvements are listed, in Section IV, the proposed change detection algorithm is discussed, in Section V, the proposed test case detection and reduction algorithm is elaborated, in the Section VI, the proposed complete automated framework is furnished, in the Section VII, the results are discussed, in the Section VIII, the comparative analysis for understanding the improvements are discussed and, this work presents the final conclusion The rest of the work is furnished such as in the Section II, the outcomes from the parallel researcher are analyzed, in Section III, problem definition and the scope for improvements are listed, in Section IV, the proposed change detection algorithm is discussed, in Section V, the proposed test case detection and reduction algorithm is elaborated, in the Section VI, the proposed complete automated framework is furnished, in the Section VII, the results are discussed, in the Section VIII, the comparative analysis for understanding the improvements are discussed and in the Section IX, this work presents the final conclusion
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