short-paper

REMI: defect prediction for efficient API testing

Authors:

Soonhwang Choi,

Sunghun KimAuthors Info & Claims

ESEC/FSE 2015: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

Pages 990 - 993

https://doi.org/10.1145/2786805.2804429

Published: 30 August 2015 Publication History

Abstract

Quality assurance for common APIs is important since the the reliability of APIs affects the quality of other systems using the APIs. Testing is a common practice to ensure the quality of APIs, but it is a challenging and laborious task especially for industrial projects. Due to a large number of APIs with tight time constraints and limited resources, it is hard to write enough test cases for all APIs. To address these challenges, we present a novel technique, REMI that predicts high risk APIs in terms of producing potential bugs. REMI allows developers to write more test cases for the high risk APIs. We evaluate REMI on a real-world industrial project, Tizen-wearable, and apply REMI to the API development process at Samsung Electronics. Our evaluation results show that REMI predicts the bug-prone APIs with reasonable accuracy (0.681 f-measure on average). The results also show that applying REMI to the Tizen-wearable development process increases the number of bugs detected, and reduces the resources required for executing test cases.

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Cited By

Kang HDo S(2024)ML-Based Software Defect Prediction in Embedded Software for Telecommunication Systems (Focusing on the Case of SAMSUNG ELECTRONICS)Electronics10.3390/electronics1309169013:9(1690)Online publication date: 26-Apr-2024
https://doi.org/10.3390/electronics13091690
Majumder SChakraborty JMenzies T(2024)When less is more: on the value of “co-training” for semi-supervised software defect predictorsEmpirical Software Engineering10.1007/s10664-023-10418-429:2Online publication date: 24-Feb-2024
https://doi.org/10.1007/s10664-023-10418-4
Huang YTang ZChen XZhou X(2024)Towards automatically identifying the co‐change of production and test codeSoftware Testing, Verification and Reliability10.1002/stvr.1870Online publication date: 11-Jan-2024
https://doi.org/10.1002/stvr.1870
Show More Cited By

Index Terms

REMI: defect prediction for efficient API testing
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Project and people management
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
  2. Software notations and tools
    1. Software configuration management and version control systems

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Published In

cover image ACM Conferences

ESEC/FSE 2015: Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

August 2015

1068 pages

ISBN:9781450336758

DOI:10.1145/2786805

General Chair:
Elisabetta Di Nitto
Politecnico di Milano, Italy
,
Program Chairs:
Mark Harman
University College London, UK
,
Patrick Heymans
University of Namur, Belgium

Copyright © 2015 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 the author(s) 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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SIGSOFT: ACM Special Interest Group on Software Engineering

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 August 2015

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ESEC/FSE'15

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SIGSOFT

ESEC/FSE'15: Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering

August 30 - September 4, 2015

Bergamo, Italy

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Cited By

Kang HDo S(2024)ML-Based Software Defect Prediction in Embedded Software for Telecommunication Systems (Focusing on the Case of SAMSUNG ELECTRONICS)Electronics10.3390/electronics1309169013:9(1690)Online publication date: 26-Apr-2024
https://doi.org/10.3390/electronics13091690
Majumder SChakraborty JMenzies T(2024)When less is more: on the value of “co-training” for semi-supervised software defect predictorsEmpirical Software Engineering10.1007/s10664-023-10418-429:2Online publication date: 24-Feb-2024
https://doi.org/10.1007/s10664-023-10418-4
Huang YTang ZChen XZhou X(2024)Towards automatically identifying the co‐change of production and test codeSoftware Testing, Verification and Reliability10.1002/stvr.1870Online publication date: 11-Jan-2024
https://doi.org/10.1002/stvr.1870
Sun WYan MLiu ZXia XLei YLo D(2023)Revisiting the Identification of the Co-evolution of Production and Test CodeACM Transactions on Software Engineering and Methodology10.1145/360718332:6(1-37)Online publication date: 30-Sep-2023
https://dl.acm.org/doi/10.1145/3607183
Do CPhyo YRiesco AOgata K(2023)Optimization Techniques for Model Checking Leads-to Properties in a Stratified WayACM Transactions on Software Engineering and Methodology10.1145/360461032:6(1-38)Online publication date: 30-Sep-2023
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Rosa GScalabrino SBavota GOliveto R(2023)What Quality Aspects Influence the Adoption of Docker Images?ACM Transactions on Software Engineering and Methodology10.1145/360311132:6(1-30)Online publication date: 30-Sep-2023
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Batoun MYung KTian YSayagh M(2023)An Empirical Study on GitHub Pull Requests’ ReactionsACM Transactions on Software Engineering and Methodology10.1145/359720832:6(1-35)Online publication date: 30-Sep-2023
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Li JLi GLi ZJin ZHu XZhang KFu Z(2023)CodeEditor: Learning to Edit Source Code with Pre-trained ModelsACM Transactions on Software Engineering and Methodology10.1145/359720732:6(1-22)Online publication date: 30-Sep-2023
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Huang QLiao DXing ZZuo ZWang CXia X(2023)Semantic-Enriched Code Knowledge Graph to Reveal Unknowns in Smart Contract Code ReuseACM Transactions on Software Engineering and Methodology10.1145/359720632:6(1-37)Online publication date: 30-Sep-2023
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Zhang MArcuri A(2023)Open Problems in Fuzzing RESTful APIs: A Comparison of ToolsACM Transactions on Software Engineering and Methodology10.1145/359720532:6(1-45)Online publication date: 30-Sep-2023
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