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Mining Effective Temporal Specifications from Heterogeneous API Data.

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Title: Mining Effective Temporal Specifications from Heterogeneous API Data.
Authors: Wu, Qian1 wuqian08@sei.pku.edu.cn, Liang, Guang-Tai1 lianggt08@sei.pku.edu.cn, Wang, Qian-Xiang1 wqx@sei.pku.edu.cn, Mei, Hong1 meihg@sei.pku.edu.cn
Source: Journal of Computer Science & Technology (10009000). Nov2011, Vol. 26 Issue 6, p1061-1075. 15p.
Subjects: Computer programming software, Computer interfaces software, Source code, Data mining, Information resources
Abstract: Temporal specifications for Application Programming Interfaces (APIs) serve as an important basis for many defect detection tools. As these specifications are often not well documented, various approaches have been proposed to automatically mine specifications typically from API library source code or from API client programs. However, the library-based approaches take substantial computational resources and produce rather limited useful specifications, while the client-based approaches suffer from high false positive rates. To address the issues of existing approaches, we propose a novel specification mining approach, called MineHEAD, which exploits heterogeneous API data, including information from API client programs as well as API library source code and comments, to produce effective specifications for defect detection with low cost. In particular, MineHEAD first applies client-based specification mining to produce a collection of candidate specifications, and then exploits the related library source code and comments to identify and refine the real specifications from the candidates. Our evaluation results on nine open source projects show that MineHEAD produces effective specifications with average precision of 97.2%. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Computer Science & Technology (10009000) is the property of Springer Nature 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.)
Database: Engineering Source
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  Data: <searchLink fieldCode="AR" term="%22Wu%2C+Qian%22">Wu, Qian</searchLink><relatesTo>1</relatesTo><i> wuqian08@sei.pku.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Liang%2C+Guang-Tai%22">Liang, Guang-Tai</searchLink><relatesTo>1</relatesTo><i> lianggt08@sei.pku.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Qian-Xiang%22">Wang, Qian-Xiang</searchLink><relatesTo>1</relatesTo><i> wqx@sei.pku.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Mei%2C+Hong%22">Mei, Hong</searchLink><relatesTo>1</relatesTo><i> meihg@sei.pku.edu.cn</i>
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  Data: <searchLink fieldCode="DE" term="%22Computer+programming+software%22">Computer programming software</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+interfaces+software%22">Computer interfaces software</searchLink><br /><searchLink fieldCode="DE" term="%22Source+code%22">Source code</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Information+resources%22">Information resources</searchLink>
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  Data: Temporal specifications for Application Programming Interfaces (APIs) serve as an important basis for many defect detection tools. As these specifications are often not well documented, various approaches have been proposed to automatically mine specifications typically from API library source code or from API client programs. However, the library-based approaches take substantial computational resources and produce rather limited useful specifications, while the client-based approaches suffer from high false positive rates. To address the issues of existing approaches, we propose a novel specification mining approach, called MineHEAD, which exploits heterogeneous API data, including information from API client programs as well as API library source code and comments, to produce effective specifications for defect detection with low cost. In particular, MineHEAD first applies client-based specification mining to produce a collection of candidate specifications, and then exploits the related library source code and comments to identify and refine the real specifications from the candidates. Our evaluation results on nine open source projects show that MineHEAD produces effective specifications with average precision of 97.2%. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Computer Science & Technology (10009000) is the property of Springer Nature 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=67480716
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        Value: 10.1007/s11390-011-1201-0
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        Text: English
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        Type: general
      – SubjectFull: Computer interfaces software
        Type: general
      – SubjectFull: Source code
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      – TitleFull: Mining Effective Temporal Specifications from Heterogeneous API Data.
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            NameFull: Liang, Guang-Tai
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              M: 11
              Text: Nov2011
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              Y: 2011
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