Synthesizing API usage examples

Buse,; Weimer,

doi:10.1109/icse.2012.6227140

Cited by 108 publications

(85 citation statements)

References 33 publications

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“…This requires developers to obtain knowledge that lies behind the API. Although several problems on API usage examples without adequate documentation or sufficient abstraction have been pointed out in [17], [18], we may leave them unsolved in the paper.…”

Section: Api Usage Patternsmentioning

confidence: 99%

Mining API Usage Patterns by Applying Method Categorization to Improve Code Completion

Akbar

Omori

Maruyama

2014

IEICE Trans. Inf. & Syst.

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SUMMARYDevelopers often face difficulties while using APIs. API usage patterns can aid them in using APIs efficiently, which are extracted from source code stored in software repositories. Previous approaches have mined repositories to extract API usage patterns by simply applying data mining techniques to the collection of method invocations of API objects. In these approaches, respective functional roles of invoked methods within API objects are ignored. The functional role represents what type of purpose each method actually achieves, and a method has a specific predefined order of invocation in accordance with its role. Therefore, the simple application of conventional mining techniques fails to produce API usage patterns that are helpful for code completion. This paper proposes an improved approach that extracts API usage patterns at a higher abstraction level rather than directly mining the actual method invocations. It embraces a multilevel sequential mining technique and uses categorization of method invocations based on their functional roles. We have implemented a mining tool and an extended Eclipse's code completion facility with extracted API usage patterns. Evaluation results of this tool show that our approach improves existing code completion.

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Section: Api Usage Patternsmentioning

confidence: 99%

Mining API Usage Patterns by Applying Method Categorization to Improve Code Completion

Akbar

Omori

Maruyama

2014

IEICE Trans. Inf. & Syst.

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“…There have been several studies which recommend libraries and APIs for developers based on their requirements (e.g., [1,2,3]), and some also find or generate samples to illustrate how to use the recommended APIs (e.g., [4,5,6,7]). However, few studies focus on recommending APIs for Android development.…”

Section: Introductionmentioning

confidence: 99%

Extracting Data from Tutorials for Android API Recommendation

Yuan¹

2018

Proceedings of the 2nd International Forum on Management, Education and Information Technology Application (IFMEITA 2017)

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Abstract. Code recommendation is becoming more and more important for software development because increasing numbers of existing libraries are being utilized by developers and code recommendation tools offer great aids to developers in quickly finding suitable APIs to use. There have been many studies on recommending various libraries and APIs for developers according to their requirements for certain functionalities, but little effort is spent in Android API recommendation. This study aims at building a database which stores the mapping co-relationships from Android APIs and their descriptive texts which describe the functionalities that can be achieved using the APIs. The data of such mapping co-relationships are extracted from online tutorials, and the database can be further used for recommending APIs for Android development by comparing the similarity between users' requirements and the descriptive texts in the database.

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“…Such tools have generally focused on automatically generating documentation where none exists [12,13,58,59]. Preliminary studies have focused on very specific classes of incomplete or inconsistent comments (specifically, only for Javadoc elements [55] and less than 30 comments each in large, open source programs [63,64]), but there exists no general approach for identifying low-quality comments of arbitrary types.…”

Section: Ensuring Documentation Completeness and Consistencymentioning

confidence: 99%

“…Studies suggest that only around half of all methods in realworld systems are commented in practice [34]. Several tools have been developed to generate documentation where none exists [12,13,58,59]. While research suggests that up to 34% of existing comments may fail to completely document the associated code, relatively little work has been done to identify or remedy such situations [55].…”

Section: Background and Related Workmentioning

confidence: 99%

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Leveraging Light-Weight Analyses to Aid Software Maintenance

Fry

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Software maintenance can account for up to 90% of a system's life cycle cost. As a result, many automated techniques have been developed to reduce the overall effort necessary to sustain software over time. While many of these tools work well under certain circumstances, we believe that they could be improved by taking advantage of large untapped sources of unstructured information that result from natural software development. As the size and complexity of systems increases, taking advantage of such previously-overlooked information is of paramount importance.The proposed research will design lightweight analyses to extract latent information encoded by humans in software development artifacts and thereby reduce the costs of software maintenance. We will design analyses that apply throughout the maintenance process, focusing on three areas: (1) triaging large collections of exposed defects; (2) guiding the search for automatic defect repairs; and (3) ensuring the continued consistency of system documentation over time.For each area of proposed research we aim to improve upon existing approaches to reduce the total cost of software maintenance while requiring minimal additional developer effort. We will evaluate the proposed work using these criteria on a diverse set of real world programs totaling millions of lines of code to concretely show the reduction of maintenance cost and improvement upon existing processes.

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Synthesizing API usage examples

Cited by 108 publications

References 33 publications

Mining API Usage Patterns by Applying Method Categorization to Improve Code Completion

Mining API Usage Patterns by Applying Method Categorization to Improve Code Completion

Extracting Data from Tutorials for Android API Recommendation

Leveraging Light-Weight Analyses to Aid Software Maintenance

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