Real world projects, real faults: Evaluating spectrum based fault localization techniques on Python projects

• Main Authors: RATNADIRA WIDYASARI, PRANA, Gede Artha Azriadi, AGUS HARYONO, Stefanus, WANG, Shaowei, LO, David
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Spectrum-based fault localization; Python; testing and debugging; empirical study; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7321; https://ink.library.smu.edu.sg/context/sis_research/article/8324/viewcontent/RealWorldProblems_Faults_av.pdf
• Institution: Singapore Management University

Spectrum Based Fault Localization (SBFL) is a statistical approach to identify faulty code within a program given a program spectra (i.e., records of program elements executed by passing and failing test cases). Several SBFL techniques have been proposed over the years, but most evaluations of those...