Abstract-Patch generation is an essential software maintenance task because most software systems inevitably have bugs that need to be fixed. Unfortunately, human resources are often insufficient to fix all reported and known bugs. To address this issue, several automated patch generation techniques have been proposed. In particular, a genetic-programming-based patch generation technique, GenProg, proposed by Weimer et al., has shown promising results. However, these techniques can generate nonsensical patches due to the randomness of their mutation operations.To address this limitation, we propose a novel patch generation approach, Pattern-based Automatic program Repair (PAR), using fix patterns learned from existing human-written patches. We manually inspected more than 60,000 human-written patches and found there are several common fix patterns. Our approach leverages these fix patterns to generate program patches automatically.

Cyclooxygenase-2 (COX-2) is a major contributor to the elevation of spinal prostaglandin E2, which augments the processing of nociceptive stimuli following peripheral inflammation, and dynorphin has been shown to have an important role in acute and chronic pain states. Moreover, the transcription factor, nuclear factor-kappa B (NF-kB), regulates the expressions of both COX-2 and dynorphin. To elucidate the role of spinal NF-kB in the induction of inflammatory pain hypersensitivity, we examined whether activated NF-kB affects pain behavior and the expressions of the mRNAs of COX-2 and prodynorphin following peripheral inflammation. Intrathecal pretreatment with different NF-kB inhibitors, namely, NF-kB decoy or pyrrolidine dithiocarbamate, significantly reduced mechanical allodynia and thermal hyperalgesia following unilateral hindpaw inflammation evoked by complete Freund's adjuvant (CFA). These NF-kB inhibitors also suppressed the activation of spinal NF-kB and the subsequent remarkable elevation of spinal COX-2 mRNA, but not that of prodynorphin mRNA. In addition, the activation of spinal NF-kB following CFA injection was inhibited by intrathecal pretreatments with interleukin-1 beta receptor antagonist or caspase-1 inhibitor. In view of the fact that interleukin-1 beta (IL-1 beta) is the major inducer of spinal COX-2 upregulation following CFA injection, our results suggest that IL-1 beta-induced spinal COX-2 upregulation and pain hypersensitivity following peripheral inflammation are mediated through the activation of the NF-kB-associated pathways.

We revisit the performance of template-based APR to build comprehensive knowledge about the effectiveness of fix patterns, and to highlight the importance of complementary steps such as fault localization or donor code retrieval. To that end, we first investigate the literature to collect, summarize and label recurrently-used fix patterns. Based on the investigation, we build TBar, a straightforward APR tool that systematically attempts to apply these fix patterns to program bugs. We thoroughly evaluate TBar on the De-fects4J benchmark. In particular, we assess the actual qualitative and quantitative diversity of fix patterns, as well as their effectiveness in yielding plausible or correct patches. Eventually, we find that, assuming a perfect fault localization, TBar correctly/plausibly fixes 74/101 bugs. Replicating a standard and practical pipeline of APR assessment, we demonstrate that TBar correctly fixes 43 bugs from Defects4J, an unprecedented performance in the literature (including all approaches, i.e., template-based, stochastic mutation-based or synthesis-based APR). CCS CONCEPTS• Software and its engineering → Software verification and validation; Software defect analysis; Software testing and debugging. KEYWORDSAutomated program repair, fix pattern, empirical assessment.

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-theart 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.