Automated API Property Inference Techniques
Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and sophisticated APIs can represent a challenge due to hidden assumptions and requirements. Numerous approaches have been developed to infer properties of...
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| Published in | IEEE transactions on software engineering Vol. 39; no. 5; pp. 613 - 637 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.05.2013
IEEE Computer Society |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and sophisticated APIs can represent a challenge due to hidden assumptions and requirements. Numerous approaches have been developed to infer properties of APIs, intended to guide their use by developers. With each approach come new definitions of API properties, new techniques for inferring these properties, and new ways to assess their correctness and usefulness. This paper provides a comprehensive survey of over a decade of research on automated property inference for APIs. Our survey provides a synthesis of this complex technical field along different dimensions of analysis: properties inferred, mining techniques, and empirical results. In particular, we derive a classification and organization of over 60 techniques into five different categories based on the type of API property inferred: unordered usage patterns, sequential usage patterns, behavioral specifications, migration mappings, and general information. |
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| AbstractList | Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and sophisticated APIs can represent a challenge due to hidden assumptions and requirements. Numerous approaches have been developed to infer properties of APIs, intended to guide their use by developers. With each approach come new definitions of API properties, new techniques for inferring these properties, and new ways to assess their correctness and usefulness. This paper provides a comprehensive survey of over a decade of research on automated property inference for APIs. Our survey provides a synthesis of this complex technical field along different dimensions of analysis: properties inferred, mining techniques, and empirical results. In particular, we derive a classification and organization of over 60 techniques into five different categories based on the type of API property inferred: unordered usage patterns, sequential usage patterns, behavioral specifications, migration mappings, and general information. [PUBLICATION ABSTRACT] Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and sophisticated APIs can represent a challenge due to hidden assumptions and requirements. Numerous approaches have been developed to infer properties of APIs, intended to guide their use by developers. With each approach come new definitions of API properties, new techniques for inferring these properties, and new ways to assess their correctness and usefulness. This paper provides a comprehensive survey of over a decade of research on automated property inference for APIs. Our survey provides a synthesis of this complex technical field along different dimensions of analysis: properties inferred, mining techniques, and empirical results. In particular, we derive a classification and organization of over 60 techniques into five different categories based on the type of API property inferred: unordered usage patterns, sequential usage patterns, behavioral specifications, migration mappings, and general information. |
| Author | Mezini, M. Robillard, M. P. Ratchford, T. Kawrykow, D. Bodden, E. |
| Author_xml | – sequence: 1 givenname: M. P. surname: Robillard fullname: Robillard, M. P. email: martin@cs.mcgill.ca organization: Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada – sequence: 2 givenname: E. surname: Bodden fullname: Bodden, E. email: bodden@ec-spride.de organization: Secure Software Eng. Group, Tech. Univ. Darmstadt, Darmstadt, Germany – sequence: 3 givenname: D. surname: Kawrykow fullname: Kawrykow, D. email: dkawry@cs.mcgill.ca organization: Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada – sequence: 4 givenname: M. surname: Mezini fullname: Mezini, M. email: mezini@informatik.tu-darmstadt.de organization: Tech. Univ. Darmstadt, Darmstadt, Germany – sequence: 5 givenname: T. surname: Ratchford fullname: Ratchford, T. email: tratch@us.ibm.com organization: IBM Res., Cambridge, MA, USA |
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| Snippet | Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and... |
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| SubjectTerms | API API evolution API property API usage pattern Application programming interface Association rules Automated Automation Categories Context Data mining Inference interface Itemsets Organizations pattern mining Programming programming rules Protocols Reusable Software engineering specifications Statistical inference Studies Use statistics |
| Title | Automated API Property Inference Techniques |
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