LNCS

Static program analyzers are increasingly effective in checking correctness properties of programs and reporting any errors found, often in the form of error traces. However, developers still spend a significant amount of time on debugging. This involves processing long error traces in an effort to localize a bug to a relatively small part of the program and to identify its cause. In this paper, we present a technique for automated fault localization that, given a program and an error trace, efficiently narrows down the cause of the error to a few statements. These statements are then ranked in terms of their suspiciousness. Our technique relies only on the semantics of the given program and does not require any test cases or user guidance. In experiments on a set of C benchmarks, we show that our technique is effective in quickly isolating the cause of error while out-performing other state-of-the-art fault-localization techniques

Automatic Debugging by Using Soft Constraints

研究成果の概要 (和文) : ソフトウェアの自動デバッグ方式の構築を目的として、プログラムの誤り特定手法に関する研究を行った。そのためのアプローチとして制約の概念を採用した。具体的には、入力、プログラム、事後条件から制約充足問題を構成した上で、違反の原因となった制約を特定し、プログラム中の対応する部分を誤り箇所の候補として提示するようにした。本研究では特に柔らかい制約を用いた自動デバッグ方式を構築し、Ｃ言語を対象とする自動デバッグツールを開発した。研究成果の概要 (英文) : We studied automatic software debugging by focusing on the localization of faults in programs. For this purpose, we adopted the notion of constraints. Specifically, we construct constraint satisfaction problems from inputs, programs, and post-conditions, and then localize constraints that cause the violations to indicate the corresponding program parts as candidates of the faults. We particularly constructed soft constraint-based methods for automatic debugging, and developed an automatic debugging tool for C programs

Computer Aided Verification

The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic