no description available

Xuanzhe Liu

Peking University

China

Chengxu Yang

Peking University

China

Ding Li

Peking University

China

Yuhan Zhou

Peking University

Shaofei Li

Peking University

Jiali Chen

Peking University

Zhenpeng Chen

University College London

United Kingdom

As software systems grow larger and more complex, debugging takes up an increasingly significant portion of developers’ time and efforts during software maintenance. To aid software engineers in debugging, many automated debugging and repair techniques have been proposed. Both the development and evaluation of these automated techniques depend on benchmarks of bugs. While many different defect benchmarks have been developed, only a few benchmarks are widely used due to the origin of the collected bugs as well as the usability of the benchmarks themselves, risking a biased research landscape. This paper presents BugsC++, a new benchmark that contains 209 real-world bugs collected from 22 open-source C/C++ projects. BugsC++ aims to provide high usability by providing a similar user interface to the widely used Defects4J. Further, BugsC++ ensures the replicability of the bugs in its collection by encapsulating each buggy program in a Docker container. By providing a highly usable real-world defect benchmark for C/C++, we hope to promote debugging research for C/C++.

Gabin An

KAIST

South Korea

Minhyuk Kwon

Suresoft Technologies

South Korea

Kyunghwa Choi

Suresoft Technologies

South Korea

Jooyong Yi

UNIST (Ulsan National Institute of Science and Technology)

South Korea

Shin Yoo

KAIST

South Korea

Large Language models (LLMs) can be induced to solve non-trivial problems with “few-shot” prompts including illustrative problem-solution examples. Now if the few-shots also include “chain of thought” (CoT) explanations, which are of the form problem-explanation-solution, LLMs will generate a “explained” solution, and perform even better. Recently an exciting, substantially better technique, self-consistency 1 has emerged, based on the intuition that there are many plausible explanations for the right solution; when the LLM is sampled repeatedly to generate a pool of explanation-solution pairs, for a given problem, the most frequently occurring solutions in the pool (ignoring the explanations) tend to be even more likely to be correct! Unfortunately, the use of this highly-performant S-C (or even CoT) approach in software engineering settings is hampered by the lack of explanations; most software datasets lack explanations. In this paper, we describe an application of the S-C approach to program repair, using the commit log on the fix as the explanation, only in the illustrative few-shots. We achieve state-of-the art results, beating previous approaches to prompting-based program repair, on the MODIT dataset; we also find evidence suggesting that the correct commit messages are helping the LLM learn to produce better patches.

Toufique Ahmed

University of California at Davis

United States

Prem Devanbu

University of California at Davis

United States

no description available

Chunqiu Steven Xia

University of Illinois at Urbana-Champaign

United States

Yifeng Ding

University of Illinois at Urbana-Champaign

United States

Lingming Zhang

University of Illinois at Urbana-Champaign

United States

no description available

Quanjun Zhang

Nanjing University

Chunrong Fang

Nanjing University

China

Tongke Zhang

Nanjing University

Bowen Yu

Nanjing University

Weisong Sun

Nanjing University

China

Zhenyu Chen

Nanjing University

China

Automated program repair (APR) approaches suffer from long patch validation time, which limits their practical application and receives relatively low attention. The patch validation process repeatedly executes tests to filter patches, and has been recognized as the dual of mutation analysis. We systematically investigate existing mutation testing techniques and recognize five families of acceleration techniques that are suitable for patch validation, two of which are never adapted to a general-purpose patch validator. We implement and demonstrate ExpressAPR, the first framework that combines five families of acceleration techniques for patch validation as the complete set. In our evaluation on 30 random Defects4J bugs and four APR systems, ExpressAPR accelerates patch validation for two orderof-magnitudes over plain validation or one order-of-magnitude over the state-of-the-art approach, benefiting APR researchers and users with a much shorter patch validation time.

Demo video available at https://youtu.be/7AB-4VvBuuM

Tool repo (source code + Docker image + evaluation dataset) available at https://github.com/ExpressAPR/ExpressAPR

Yuan-An Xiao

Peking University

China

Chenyang Yang

Peking University

Bo Wang

Beijing Jiaotong University

China

Yingfei Xiong

Peking University

China