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Predicting faults from cached history

Published: 19 February 2008 Publication History

Abstract

We analyze the version history of 7 software systems to predict the most fault prone entities and files. The basic assumption is that faults do not occur in isolation, but rather in bursts of several related faults. Therefore, we cache locations that are likely to have faults: starting from the location of a known (fixed) fault, we cache the location itself, any locations changed together with the fault, recently added locations, and recently changed locations. By consulting the cache at the moment a fault is fixed, a developer can detect likely fault-prone locations. This is useful for prioritizing verification and validation resources on the most fault prone files or entities. In our evaluation of seven open source projects with more than 200,000 revisions, the cache selects 10% of the source code files; these files account for 73%-95% of faults--a significant advance beyond the state of the art

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  • (2024)PromptLink: Multi-template prompt learning with adversarial training for issue-commit link recoveryProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3690751(461-467)Online publication date: 24-Oct-2024
  • (2023)Empirical Study: How Issue Classification Influences Software Defect PredictionIEEE Access10.1109/ACCESS.2023.324204511(11732-11748)Online publication date: 2023
  • (2022)Multi-Dimension Convolutional Neural Network for Bug LocalizationIEEE Transactions on Services Computing10.1109/TSC.2020.300621415:3(1649-1663)Online publication date: 1-May-2022
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Published In

cover image ACM Conferences
ISEC '08: Proceedings of the 1st India software engineering conference
February 2008
164 pages
ISBN:9781595939173
DOI:10.1145/1342211
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 February 2008

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Author Tags

  1. bug
  2. cache
  3. fault
  4. locality
  5. prediction

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  • Invited-talk

Conference

ISEC08
Sponsor:
ISEC08: India Software Engineering Conference
February 19 - 22, 2008
Hyderabad, India

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Overall Acceptance Rate 76 of 315 submissions, 24%

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Cited By

View all
  • (2024)PromptLink: Multi-template prompt learning with adversarial training for issue-commit link recoveryProceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement10.1145/3674805.3690751(461-467)Online publication date: 24-Oct-2024
  • (2023)Empirical Study: How Issue Classification Influences Software Defect PredictionIEEE Access10.1109/ACCESS.2023.324204511(11732-11748)Online publication date: 2023
  • (2022)Multi-Dimension Convolutional Neural Network for Bug LocalizationIEEE Transactions on Services Computing10.1109/TSC.2020.300621415:3(1649-1663)Online publication date: 1-May-2022
  • (2021)Just‐in‐time defect prediction for software hunksSoftware: Practice and Experience10.1002/spe.300152:1(130-153)Online publication date: 16-Jun-2021
  • (2019)Is Bigger Data Better for Defect Prediction: Examining the Impact of Data Size on Supervised and Unsupervised Defect PredictionWeb Information Systems and Applications10.1007/978-3-030-30952-7_16(138-150)Online publication date: 16-Sep-2019
  • (2018)Connecting software metrics across versions to predict defects2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER)10.1109/SANER.2018.8330212(232-243)Online publication date: Mar-2018
  • (2018)An empirical study of software change classification with imbalance data‐handling methodsSoftware: Practice and Experience10.1002/spe.260648:11(1968-1999)Online publication date: 29-Jun-2018
  • (2017)On the Effectiveness of Bug Predictors with Procedural SystemsProceedings of the 20th International Conference on Fundamental Approaches to Software Engineering - Volume 1020210.1007/978-3-662-54494-5_5(78-95)Online publication date: 22-Apr-2017
  • (2015)Do developers respond to code stability warnings?Proceedings of the 25th Annual International Conference on Computer Science and Software Engineering10.5555/2886444.2886469(162-170)Online publication date: 2-Nov-2015
  • (2015)To what extent could we detect field defects? An extended empirical study of false negatives in static bug-finding toolsAutomated Software Engineering10.1007/s10515-014-0169-822:4(561-602)Online publication date: 1-Dec-2015
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