FixMiner: Mining Relevant Fix Patterns for Automated Program Repair

Patching is a common activity in software development. It is generally performed on a source code base to address bugs or add new functionalities. In this context, given the recurrence of bugs across projects, the associated similar patches can be leveraged to extract generic fix actions. While the literature includes various approaches leveraging similarity among patches to guide program repair, these approaches often do not yield fix patterns that are tractable and reusable as actionable input to APR systems. In this paper, we propose a systematic and automated approach to mining relevant and actionable fix patterns based on an iterative clustering strategy applied to atomic changes within patches. The goal of FixMiner is thus to infer separate and reusable fix patterns that can be leveraged in other patch generation systems. Our technique, FixMiner, leverages Rich Edit Script which is a specialized tree structure of the edit scripts that captures the AST-level context of the code changes. FixMiner uses different tree representations of Rich Edit Scripts for each round of clustering to identify similar changes. These are abstract syntax trees, edit actions trees, and code context trees. We have evaluated FixMiner on thousands of software patches collected from open source projects. Preliminary results show that we are able to mine accurate patterns, efficiently exploiting change information in Rich Edit Scripts. We further integrated the mined patterns to an automated program repair prototype, PARFixMiner, with which we are able to correctly fix 26 bugs of the Defects4J benchmark. Beyond this quantitative performance, we show that the mined fix patterns are sufficiently relevant to produce patches with a high probability of correctness: 81% of PARFixMiner's generated plausible patches are correct.Comment: 31 pages, 11 figure

You Cannot Fix What You Cannot Find! An Investigation of Fault Localization Bias in Benchmarking Automated Program Repair Systems

Properly benchmarking Automated Program Repair (APR) systems should contribute to the development and adoption of the research outputs by practitioners. To that end, the research community must ensure that it reaches significant milestones by reliably comparing state-of-the-art tools for a better understanding of their strengths and weaknesses. In this work, we identify and investigate a practical bias caused by the fault localization (FL) step in a repair pipeline. We propose to highlight the different fault localization configurations used in the literature, and their impact on APR systems when applied to the Defects4J benchmark. Then, we explore the performance variations that can be achieved by `tweaking' the FL step. Eventually, we expect to create a new momentum for (1) full disclosure of APR experimental procedures with respect to FL, (2) realistic expectations of repairing bugs in Defects4J, as well as (3) reliable performance comparison among the state-of-the-art APR systems, and against the baseline performance results of our thoroughly assessed kPAR repair tool. Our main findings include: (a) only a subset of Defects4J bugs can be currently localized by commonly-used FL techniques; (b) current practice of comparing state-of-the-art APR systems (i.e., counting the number of fixed bugs) is potentially misleading due to the bias of FL configurations; and (c) APR authors do not properly qualify their performance achievement with respect to the different tuning parameters implemented in APR systems.Comment: Accepted by ICST 201

Teaching programming at a distance: the Internet software visualization laboratory

This paper describes recent developments in our approach to teaching computer programming in the context of a part-time Masters course taught at a distance. Within our course, students are sent a pack which contains integrated text, software and video course material, using a uniform graphical representation to tell a consistent story of how the programming language works. The students communicate with their tutors over the phone and through surface mail. Through our empirical studies and experience teaching the course we have identified four current problems: (i) students' difficulty mapping between the graphical representations used in the course and the programs to which they relate, (ii) the lack of a conversational context for tutor help provided over the telephone, (iii) helping students who due to their other commitments tend to study at 'unsociable' hours, and (iv) providing software for the constantly changing and expanding range of platforms and operating systems used by students. We hope to alleviate these problems through our Internet Software Visualization Laboratory (ISVL), which supports individual exploration, and both synchronous and asynchronous communication. As a single user, students are aided by the extra mappings provided between the graphical representations used in the course and their computer programs, overcoming the problems of the original notation. ISVL can also be used as a synchronous communication medium whereby one of the users (generally the tutor) can provide an annotated demonstration of a program and its execution, a far richer alternative to technical discussions over the telephone. Finally, ISVL can be used to support asynchronous communication, helping students who work at unsociable hours by allowing the tutor to prepare short educational movies for them to view when convenient. The ISVL environment runs on a conventional web browser and is therefore platform independent, has modest hardware and bandwidth requirements, and is easy to distribute and maintain. Our planned experiments with ISVL will allow us to investigate ways in which new technology can be most appropriately applied in the service of distance education