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1. A Comprehensive Empirical Investigation on Failure Clustering in Parallel Debugging
Accession number: 20220296741
Authors: Song, Yi (1); Xie, Xiaoyuan (1); Liu, Quanming (1); Zhang, Xihao (1); Wu, Xi (1)
Author affiliation: (1) School of Computer Science, Wuhan University, China
Corresponding author: Xie, Xiaoyuan(xxie@whu.edu.cn)
Source title: arXiv
Abbreviated source title: arXiv
Issue date: July 16, 2022
Publication year: 2022
Language: English
E-ISSN: 23318422
Document type: Preprint (PP)
Publisher: arXiv
Abstract: The clustering technique has attracted a lot of attention as a promising strategy for parallel debugging in
multi-fault scenarios, this heuristic approach (i.e., failure indexing or fault isolation) enables developers to perform
multiple debugging tasks simultaneously through dividing failed test cases into several disjoint groups. When
using statement ranking representation to model failures for better clustering, several factors influence clustering
effectiveness, including the risk evaluation formula (REF), the number of faults (NOF), the fault type (FT), and the
number of successful test cases paired with one individual failed test case (NSP1F). In this paper, we present the first
comprehensive empirical study of how these four factors influence clustering effectiveness. We conduct extensive
controlled experiments on 1060 faulty versions of 228 simulated faults and 141 real faults, and the results reveal
that: 1) GP19 is highly competitive across all REFs, 2) clustering effectiveness decreases as NOF increases, 3)
higher clustering effectiveness is easier to achieve when a program contains only predicate faults, and 4) clustering
effectiveness remains when the scale of NSP1F is reduced to 20%. Copyright © 2022, The Authors. All rights reserved.
Number of references: 81
Main heading: Heuristic methods
Controlled terms: Program debugging
Uncontrolled terms: Clustering techniques - Clusterings - Empirical investigation - Failure clustering - Fault
isolation - Fault scenarios - Multi faults - Multiple faults - Parallel debugging - Test case
Classification code: 723.1 Computer Programming
Numerical data indexing: Percentage 2.00E+01%
DOI: 10.48550/arXiv.2207.07992
Compendex references: YES
Preprint ID: 2207.07992v2
Preprint source website: https://arxiv.org
Preprint ID type: ARXIV
Database: Compendex
Data Provider: Engineering Village
Compilation and indexing terms, Copyright 2023 Elsevier Inc.
Content provided by Engineering Village. Copyright 2023
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