Semantic fault localization and suspiciousness ranking

Abstract: 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

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch
• Anmerkungen: Tools and algorithms for the construction and analysis of systems. - Cham, 2019. - 226-243, ISBN: 9783030174613

Beteiligte, Orts- und Zeitangaben

• Ereignis: Veröffentlichung
• (wo): Freiburg
• (wer): Universität
• (wann): 2021
• Urheber: Christakis, Maria ; Heizmann, Matthias ; Mansur, Muhammad Numair ; Schilling, Christian ; Wüstholz, Valentin Tobias
• Beteiligte Personen und Organisationen: Albert-Ludwigs-Universität Freiburg. Softwaretechnik

Weitere Informationen

• DOI: 10.1007/978-3-030-17462-0_13
• URN: urn:nbn:de:bsz:25-freidok-1761481
• Rechteinformation: Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:21 MESZ