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Impact Analysis of Cross-Project Bugs on Software Ecosystems.

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Title: Impact Analysis of Cross-Project Bugs on Software Ecosystems.
Authors: Wanwangying Ma1 wwyma@smail.nju.edu.cn, Lin Chen1 lchen@nju.edu.cn, Xiangyu Zhang2 xyzhang@cs.purdue.edu, Yang Feng3, Zhaogui Xu3, Zhifei Chen3, Yuming Zhou1 zhouyuming@nju.edu.cn, Baowen Xu1 bwxu@nju.edu.cn
Source: ICSE: International Conference on Software Engineering. 6/17/2020, p100-111. 12p.
Subjects: Software ecosystems, Computer programming, Artificial intelligence, Software engineering, Python programming language
Abstract: Software projects are increasingly forming social-technical ecosystems within which individual projects rely on the infrastructures or functional components provided by other projects, leading to complex inter-dependencies. Through inter-project dependencies, a bug in an upstream project may have profound impact on a large number of downstream projects, resulting in cross-project bugs. This emerging type of bugs has brought new challenges in bug fixing due to their unclear influence on downstream projects. In this paper, we present an approach to estimating the impact of a cross-project bug within its ecosystem by identifying the affected downstream modules (classes/methods). Note that a downstream project that uses a buggy upstream function may not be affected as the usage does not satisfy the failure inducing preconditions. For a reported bug with the known root cause function and failure inducing preconditions, we first collect the candidate downstream modules that call the upstream function through an ecosystem-wide dependence analysis. Then, the paths to the call sites of the buggy upstream function are encoded as symbolic constraints. Solving the constraints, together with the failure inducing preconditions, identifies the affected downstream modules. Our evaluation of 31 existing upstream bugs on the scientific Python ecosystem containing 121 versions of 22 popular projects (with a total of 16 millions LOC) shows that the approach is highly effective: from the 25490 candidate downstream modules that invoke the buggy upstream functions, it identifies 1132 modules where the upstream bugs can be triggered, pruning 95.6% of the candidates. The technique has no false negatives and an average false positive rate of 7.9%. Only 49 downstream modules (out of the 1132 we found) were reported before to be affected. [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Impact Analysis of Cross-Project Bugs on Software Ecosystems.
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  Data: <searchLink fieldCode="AR" term="%22Wanwangying+Ma%22">Wanwangying Ma</searchLink><relatesTo>1</relatesTo><i> wwyma@smail.nju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Lin+Chen%22">Lin Chen</searchLink><relatesTo>1</relatesTo><i> lchen@nju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Xiangyu+Zhang%22">Xiangyu Zhang</searchLink><relatesTo>2</relatesTo><i> xyzhang@cs.purdue.edu</i><br /><searchLink fieldCode="AR" term="%22Yang+Feng%22">Yang Feng</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhaogui+Xu%22">Zhaogui Xu</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Zhifei+Chen%22">Zhifei Chen</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Yuming+Zhou%22">Yuming Zhou</searchLink><relatesTo>1</relatesTo><i> zhouyuming@nju.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Baowen+Xu%22">Baowen Xu</searchLink><relatesTo>1</relatesTo><i> bwxu@nju.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 6/17/2020, p100-111. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Software+ecosystems%22">Software ecosystems</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+programming%22">Computer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Software+engineering%22">Software engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Python+programming+language%22">Python programming language</searchLink>
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  Data: Software projects are increasingly forming social-technical ecosystems within which individual projects rely on the infrastructures or functional components provided by other projects, leading to complex inter-dependencies. Through inter-project dependencies, a bug in an upstream project may have profound impact on a large number of downstream projects, resulting in cross-project bugs. This emerging type of bugs has brought new challenges in bug fixing due to their unclear influence on downstream projects. In this paper, we present an approach to estimating the impact of a cross-project bug within its ecosystem by identifying the affected downstream modules (classes/methods). Note that a downstream project that uses a buggy upstream function may not be affected as the usage does not satisfy the failure inducing preconditions. For a reported bug with the known root cause function and failure inducing preconditions, we first collect the candidate downstream modules that call the upstream function through an ecosystem-wide dependence analysis. Then, the paths to the call sites of the buggy upstream function are encoded as symbolic constraints. Solving the constraints, together with the failure inducing preconditions, identifies the affected downstream modules. Our evaluation of 31 existing upstream bugs on the scientific Python ecosystem containing 121 versions of 22 popular projects (with a total of 16 millions LOC) shows that the approach is highly effective: from the 25490 candidate downstream modules that invoke the buggy upstream functions, it identifies 1132 modules where the upstream bugs can be triggered, pruning 95.6% of the candidates. The technique has no false negatives and an average false positive rate of 7.9%. Only 49 downstream modules (out of the 1132 we found) were reported before to be affected. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1145/3377811.3380442
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        Text: English
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      – SubjectFull: Software ecosystems
        Type: general
      – SubjectFull: Computer programming
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Software engineering
        Type: general
      – SubjectFull: Python programming language
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      – TitleFull: Impact Analysis of Cross-Project Bugs on Software Ecosystems.
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            NameFull: Wanwangying Ma
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              Text: 6/17/2020
              Type: published
              Y: 2020
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            – TitleFull: ICSE: International Conference on Software Engineering
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