A Learning-to-Rank Based Fault Localization Approach using Likely Invariants

A Learning-to-Rank Based Fault Localization Approach using Likely Invariants

Debugging is a costly process that consumes much of developer time and energy. To help reduce debugging effort, many studies have proposed various fault localization approaches. These approaches take as input a set of test cases (some failing, some passing) and produce a ranked list of program eleme...

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Bibliographic Details
Main Authors: LE, Tien-Duy B., David LO, LE GOUES, Claire, GRUNSKE, Lars
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2016
Subjects:
Learning to Rank
Program Invariants
Automated Debugging
Software Engineering
Theory and Algorithms
Online Access:https://ink.library.smu.edu.sg/sis_research/3453
https://ink.library.smu.edu.sg/context/sis_research/article/4454/viewcontent/164___A_Learning_to_Rank_Based_Fault_Localization_Approach_using_Likely_Invariants__ISSTA2016_.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/3453
https://ink.library.smu.edu.sg/context/sis_research/article/4454/viewcontent/164___A_Learning_to_Rank_Based_Fault_Localization_Approach_using_Likely_Invariants__ISSTA2016_.pdf

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