A Two-stage Method of Synchronization Prediction Framework in TDD

Test-driven development (TDD) is an agile development technology that involves the running of many test cases. The software is likely to be more robust when more test cases are executed in a certain time. For this, it is important to find the combination of number of threads and test cases which can lead to the lowest average running time of a single test case aiming at different computers. It can improve the performance of TDD. Therefore, this paper proposed a method for this problem. First, it tests the three-dimensional raw data in computers with different cores. Second, these data are changed into two-dimensional data after dimensionality reduction. This facilitates the generation of fitting functions. Also, the fitting function of new data waiting for prediction is generated after dimensionality reduction. At last, similarity calculations between the fitting functions of raw and new data are carried out by using Euclidean distance similarity algorithms. Experimental results show that data based on dual-core computer have higher similarities with four new data, such as 81.25%, 100%, 81.25%, and 100%. Thus, the data of dual-core computer have higher reference credibility in predicting the average running time of a single test case of the new data for different computers. In summary, the performance of TDD can be improved after applying the proposed method.