Delta debugging has been proposed for failure-inducing changes identification. Despite promising results, there are two practical factors that thwart the application of delta debugging: large number of tests and misleading false positives. To address the issues, we present a combination of coverage analysis and delta debugging that automatically isolates failure-inducing changes. Evaluations on twelve real regressions in GNU software demonstrate both the speed gain and effectiveness improvements.

Software debugging starts with bug reports. Test engineers confirm bugs and determine the corresponding developers to fix them. However, the analysis of bug reports is time-consuming and manual inspection is difficult and tedious. To improve the efficiency of the whole process, we propose a bug processing framework that integrates bug report analysis and fault localization. An instance of the framework is implemented for regression faults. Preliminary results on a large open source application demonstrate both efficiency and effectiveness.

Script-based automated regression testing is widely used in industry. In this work, we focus on failed tests in a real regression test project. The causes of 197 failed tests produced in automated testing are examined and categorized based on an analysis procedure presented. The result shows that incorrect scripts, oracle mismatches, test tool bugs and misconfigurations involved in testing contribute most to failures instead of product bugs. Detecting and fixing the false positives are laborious and time-consuming. Our empirical study demonstrates that the benefits of test automation are obliterated to some extent in practical settings. To improve the effectiveness of automated regression testing further, some issues should receive more attention.

Regression faults are inevitably introduced in software development. Identifying and fixing regression faults can be tedious and time-consuming. The goal of my doctoral research is to provide an automated practical technique to effectively and efficiently locating failure-inducing changes. In this work, the research problem and related work is discussed first. Then, our approach, research questions, completed work and future work is presented. Finally, the expected contributions of my thesis are listed.

During software evolution, new released versions still contain many bugs. One common scenario is that end users encounter regression faults and submit them to bug tracking systems. Different from in-house regression testing, typically only one test input is available, which passes the old version and fails the modified new version. To address the issue, delta debugging has been proposed for failure-inducing changes identification between two versions. Despite promising results, there are two practical factors that thwart the application of delta debugging: a large number of tests and misleading false positives. In this work, we present a combination of coverage analysis and delta debugging that automatically isolates failure-inducing changes. Evaluations on twelve real regression faults in GNU software demonstrate both the speed gain and effectiveness improvements. Moreover, a case study on libPNG and TCPflow indicates that our technique is comparable to peer techniques in debugging regressions faults.

Recent studies have demonstrated that language-based static analysis is capable of finding hundreds of bugs in complex real systems. Such static analysis allows users to specify properties in a specification language on demand. Paths in control flow graphs are explored exhaustively against user-defined properties. To avoid the potential path explosion problem, many techniques have been used in practice such as summaries. In this paper, we investigate how to simplify programs under check utilizing user-specified properties. From our observations, most functions under check are irrelevant to given properties. Checking those functions is time consuming. A program sifting approach is proposed to select functions related to properties. Sifters are derived from user-specified properties automatically. Functions matched or affected by sifters are safely preserved while the others are safely removed. We implemented a tool SIFT and carried out some experiments. Results show that SIFT is capable of simplifying program under check remarkably with small cost. In our experiments, 85% functions in files are sifted out while 89% analysis time is saved on average.