Addressing the cause of software regressions is an important but difficult task, and has not been well studied. Current tools have some limitations, such as low detection accuracy. In this paper, we try to address these limitations and improve the accuracy of locating causes of software regressions by proposing three new techniques. Our techniques are based on tracing source code changes during program execution. Moreover, we extend current benchmark to a new programming language, and compare our techniques against existing techniques on the extended benchmark. Our new techniques outperform existing techniques in accurately locating the root cause of software regressions. Specifically, our LLMpowered technique achieves the state-of-the-art accuracy.

Software regression has been a persistent issue in software development. Although numerous techniques have been proposed to prevent regression from being introduced before release, few are available to address regression as it occurs post-release. Therefore, identifying the root cause of regression has always been a time-consuming and labor-intensive task. We aim to deliver automated solutions for solving regressions based on tracing. We present Bugfox, a trace-based analyzer that reports functions as the possible cause of regression in JavaScript. The idea is to generate runtime trace with instrumented programs, then extract the differences between clean and regression traces, and apply two heuristic strategies based on invocation order and frequency to identify the suspicious functions among differences. We evaluate our approach on 12 real-world regressions taken from the benchmark BugsJS. First strategy solves 6 regressions, and second strategy solves other 4 regressions, resulting in an overall accuracy of 83% on test cases. Notably, Bugfox solves each regression in under 1 minute with minimal memory overhead (<200 Megabytes). Our findings suggest Bugfox could help developers solve regression in real development.

Pair-wise testing is a practical and effective method which has already been used in the software testing. Extensive research has been made on the generation of pair-wise testing. In order to make it easy to analyze the current generation methods, we propose a method to ease the process. That is we transform the problem of pair-wise testing to a graphic one. The IPO algorithm is based on parameters and can ensure the optimization of test cases in each expansion. Though it has many advantages, it is still not sustainable enough because of its flexibility. We studied the three elements which affect its sustainability. The three elements are the horizontal growth of pair-wise testing, the combination of pair-wise testing cases and the extension sequence of the parameters to be extended. Thus we propose a HIPO algorithm based on IPO algorithm to solve those problems. The HIPO algorithm inherits the merits of high extension of IPO algorithm and introduces a new concept of contribution extent. It adopts the methods of preferential sequence as well as minimization algorithm to optimize the problems above. We develop the test case generation tool based on the HIPO algorithm by means of .Net technology. And we also prove its effectiveness in our experiment.