research-article

Data-guided repair of selection statements

Authors:

Divya Gopinath,

Sarfraz Khurshid,

Diptikalyan Saha,

Satish ChandraAuthors Info & Claims

ICSE 2014: Proceedings of the 36th International Conference on Software Engineering

Pages 243 - 253

https://doi.org/10.1145/2568225.2568303

Published: 31 May 2014 Publication History

Abstract

Database-centric programs form the backbone of many enterprise systems. Fixing defects in such programs takes much human effort due to the interplay between imperative code and database-centric logic. This paper presents a novel data-driven approach for automated fixing of bugs in the selection condition of database statements (e.g., WHERE clause of SELECT statements) – a common form of bugs in such programs. Our key observation is that in real-world data, there is information latent in the distribution of data that can be useful to repair selection conditions efficiently. Given a faulty database program and input data, only a part of which induces the defect, our novelty is in determining the correct behavior for the defect-inducing data by taking advantage of the information revealed by the rest of the data. We accomplish this by employing semi-supervised learning to predict the correct behavior for defect-inducing data and by patching up any inaccuracies in the prediction by a SAT-based combinatorial search. Next, we learn a compact decision tree for the correct behavior, including the correct behavior on the defect-inducing data. This tree suggests a plausible fix to the selection condition. We demonstrate the feasibility of our approach on seven realworld examples.

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Cited By

Sharma TKechagia MGeorgiou STiwari RVats IMoazen HSarro F(2024)A survey on machine learning techniques applied to source codeJournal of Systems and Software10.1016/j.jss.2023.111934209:COnline publication date: 14-Mar-2024
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Data-guided repair of selection statements

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Published In

cover image ACM Conferences

ICSE 2014: Proceedings of the 36th International Conference on Software Engineering

May 2014

1139 pages

ISBN:9781450327565

DOI:10.1145/2568225

General Chair:
Pankaj Jalote
IIIT-Delhi, India
,
Program Chairs:
Lionel Briand
University of Luxembourg, Luxembourg
,
André van der Hoek
University of California, Irvine, USA

Copyright © 2014 ACM.

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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TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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New York, NY, United States

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Published: 31 May 2014

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ICSE '14: 36th International Conference on Software Engineering

May 31 - June 7, 2014

Hyderabad, India

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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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Cited By

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https://dl.acm.org/doi/10.1016/j.jss.2023.111934
Pasupuleti KLi JSu HZiauddin M(2023)Automatic SQL Error Mitigation in OracleProceedings of the VLDB Endowment10.14778/3611540.361156816:12(3835-3847)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.14778/3611540.3611568
Yang YXia XLo DBi TGrundy JYang X(2022)Predictive Models in Software Engineering: Challenges and OpportunitiesACM Transactions on Software Engineering and Methodology10.1145/350350931:3(1-72)Online publication date: 9-Apr-2022
https://dl.acm.org/doi/10.1145/3503509
Suárez-Otero PMior MSuárez-Cabal MTuya J(2021)An Integrated Approach for Column-Oriented Database Application Evolution Using Conceptual ModelsAdvances in Conceptual Modeling10.1007/978-3-030-88358-4_3(26-32)Online publication date: 18-Oct-2021
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Usman MWang WVasic MWang KVikalo HKhurshid SDonaldson ATorlak E(2020)A study of the learnability of relational properties: model counting meets machine learning (MCML)Proceedings of the 41st ACM SIGPLAN Conference on Programming Language Design and Implementation10.1145/3385412.3386015(1098-1111)Online publication date: 11-Jun-2020
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Lou YChen JZhang LHao DZhang LZhang DMøller A(2019)History-driven build failure fixing: how far are we?Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3293882.3330578(43-54)Online publication date: 10-Jul-2019
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Li XLi WZhang YZhang LZhang DMøller A(2019)DeepFL: integrating multiple fault diagnosis dimensions for deep fault localizationProceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3293882.3330574(169-180)Online publication date: 10-Jul-2019
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Wang KSullivan AKhurshid SHuchard MKästner CFraser G(2018)Automated model repair for AlloyProceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering10.1145/3238147.3238162(577-588)Online publication date: 3-Sep-2018
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Monperrus M(2018)Automatic Software RepairACM Computing Surveys10.1145/310590651:1(1-24)Online publication date: 23-Jan-2018
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