02:00 - 02:30

• DirectFix: Looking for Simple Program Repairs

  Recent advances in program repair techniques have raised the possibility of patching bugs automatically. For an automatically generated patch to be accepted by developers, it should not only resolve the bug but also satisfy certain human-related factors including readability and comprehensibility. In this paper, we focus on the simplicity of patches (the size of changes). We present a novel semantics-based repair method that generates the simplest patch such that the program structure of the buggy program is maximally preserved. To take into account the simplicity of repairs in an efficient way (i.e., Without explicitly enumerating each repair candidate for each fault location), our method fuses fault localization and repair generation into one step. We do so by leveraging partial Max SAT constraint solving and component-based program synthesis. We compare our prototype implementation, Direct Fix, with the state-of-the-art semantics-based repair tool Sem Fix, that performs fault localization before repair generation. In our experiments with SIR programs and GNU Coreutils, Direct Fix generates repairs that are simpler than those generated by Sem Fix. Since both Direct Fix and Sem Fix are test-driven repair tools, they can introduce regressions for other tests which do not drive the repair. We found that Direct Fix causes substantially less regression errors than Sem Fix.

Sergey Mechtaev; Jooyong Yi; Abhik Roychoudhury

National University of Singapore, Singapore

02:30 - 03:00

• Safe Memory-Leak Fixing for C Programs

  Automatic bug fixing has become a promising direction for reducing manual effort in debugging. However, general approaches to automatic bug fixing may face some fundamental difficulties. In this paper, we argue that automatic fixing of specific types of bugs can be a useful complement. This paper reports our first attempt towards automatically fixing memory leaks in C programs. Our approach generates only safe fixes, which are guaranteed not to interrupt normal execution of the program. To design such an approach, we have to deal with several challenging problems such as inter-procedural leaks, global variables, loops, and leaks from multiple allocations. We propose solutions to all the problems and integrate the solutions into a coherent approach. We implemented our inter-procedural memory leak fixing into a tool named Leak Fix and evaluated Leak Fix on 15 programs with 522k lines of code. Our evaluation shows that Leak Fix is able to successfully fix a substantial number of memory leaks, and Leak Fix is scalable for large applications.

Qing Gao; Yingfei Xiong; Yaqing Mi; Lu Zhang; Weikun Yang; Zhaoping Zhou; Bing Xie; Hong Mei

Peking University, China

03:00 - 03:30

• Relifix: Automated Repair of Software Regressions

  Regression occurs when code changes introduce failures in previously passing test cases. As software evolves, regressions may be introduced. Fixing regression errors manually is time-consuming and error-prone. We propose an approach of automated repair of software regressions, called relifix, that considers the regression repair problem as a problem of reconciling problematic changes. Specifically, we derive a set of code transformations obtained from our manual inspection of 73 real software regressions, this set of code transformations uses syntactical information from changed statements. Regression repair is then accomplished via a search over the code transformation operators – which operator to apply, and where. Our evaluation compares the repair ability of relifix with Gen Prog on 35 real regression errors. Relifix repairs 23 bugs, while Gen Prog only fixes five bugs. We also measure the likelihood of both approaches in introducing new regressions given a reduced test suite. Our experimental results shows that our approach is less likely to introduce new regressions than Gen Prog.

Shin Hwei Tan; Abhik Roychoudhury

National University of Singapore, Singapore