Mining succinct and high-coverage API usage patterns from source code

Wang, Jue; Dang, Yingnong; Zhang, Hongyu; Chen, Kai; Xie, Tao; Zhang, Dongmei

doi:10.1109/msr.2013.6624045

Cited by 153 publications

(136 citation statements)

References 23 publications

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“…However, as mentioned earlier, the current implementation of PPA does not scale. More recently, tools such as UP-Miner (Wang et al 2013) have been developed to mine high coverage usage patterns from open source repositories by using multiple clustering steps. In 2015, Saied et alproposed a tool called MLUP to improve upon the clustering of MAPO (Saied et al 2015).…”

Section: Comparison To Existing Techniquesmentioning

confidence: 99%

fine-GRAPE: fine-grained APi usage extractor – an approach and dataset to investigate API usage

Sawant

Bacchelli

2016

Empir Software Eng

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An Application Programming Interface (API) provides a set of functionalities to a developer with the aim of enabling reuse. APIs have been investigated from different angles such as popularity usage and evolution to get a better understanding of their various characteristics. For such studies, software repositories are mined for API usage examples. However, many of the mining algorithms used for such purposes do not take type information into account. Thus making the results unreliable. In this paper, we aim to rectify this by introducing fine-GRAPE, an approach that produces fine-grained API usage information by taking advantage of type information while mining API method invocations and annotation. By means of fine-GRAPE, we investigate API usages from Java projects hosted on GitHub. We select five of the most popular APIs across GitHub Java projects and collect historical API usage information by mining both the release history of these APIs and the code history of every project that uses them. We perform two case studies on the resulting dataset. The first measures the lag time of each client. The second investigates the percentage of used API features. In the first case we find that for APIs that release more frequently clients are far less likely to upgrade to a more recent version of the API as opposed to clients of APIs that release infrequently. The second case study shows us that for most APIs there is a small number of features that is actually used and most of these features relate to those that have been introduced early in the APIs lifecycle.

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Section: Comparison To Existing Techniquesmentioning

confidence: 99%

fine-GRAPE: fine-grained APi usage extractor – an approach and dataset to investigate API usage

Sawant

Bacchelli

2016

Empir Software Eng

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“…Popularity of a solution is a key criterion that cannot be ignored to avoid poor quality result set, e.g., [37]. There are other factors affecting the ranking process, e.g., textual similarity.…”

Section: Searching and Ranking Working Code Examplesmentioning

confidence: 99%

“…It is not common in software development to explicitly document code examples [15][35] [37]. Programmers have to search for code examples through previously written projects and publicly available code repositories on the Internet (e.g., sourceforge.net).…”

Section: Introductionmentioning

confidence: 99%

Spotting working code examples

Keivanloo

Rilling

Zou

2014

Proceedings of the 36th International Conference on Software Engineering

128

106

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Working code examples are useful resources for pragmatic reuse in software development. A working code example provides a solution to a specific programming problem. Earlier studies have shown that existing code search engines are not successful in finding working code examples. They fail in ranking high quality code examples at the top of the result set. To address this shortcoming, a variety of pattern-based solutions are proposed in the literature. However, these solutions cannot be integrated seamlessly in Internet-scale source code engines due to their high time complexity or query language restrictions. In this paper, we propose an approach for spotting working code examples which can be adopted by Internet-scale source code search engines. The time complexity of our approach is as low as the complexity of existing code search engines on the Internet and considerably lower than the pattern-based approaches supporting free-form queries. We study the performance of our approach using a representative corpus of 25,000 open source Java projects. Our findings support the feasibility of our approach for Internet-scale code search. We also found that our approach outperforms Ohloh Code search engine, previously known as Koders, in spotting working code examples.

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“…Such perspective is also adopted in traditional program analysis, e.g., API usage pattern mining [53], [54]. As the representations can be composited by atomic symbols (e.g., AST nodes), we can also apply deep learning approaches to sequences of statements.…”

Section: A Different Perspectives For Program Modelingmentioning

confidence: 99%

Building Program Vector Representations for Deep Learning

Peng

Mou

et al. 2015

Knowledge Science, Engineering and Management

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Abstract-Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, which are the premise of deep learning for program analysis. Our representation learning approach directly makes deep learning a reality in this new field. We evaluate the learned vector representations both qualitatively and quantitatively. We conclude, based on the experiments, the coding criterion is successful in building program representations. To evaluate whether deep learning is beneficial for program analysis, we feed the representations to deep neural networks, and achieve higher accuracy in the program classification task than "shallow" methods, such as logistic regression and the support vector machine. This result confirms the feasibility of deep learning to analyze programs. It also gives primary evidence of its success in this new field. We believe deep learning will become an outstanding technique for program analysis in the near future.

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Mining succinct and high-coverage API usage patterns from source code

Cited by 153 publications

References 23 publications

fine-GRAPE: fine-grained APi usage extractor – an approach and dataset to investigate API usage

fine-GRAPE: fine-grained APi usage extractor – an approach and dataset to investigate API usage

Spotting working code examples

Building Program Vector Representations for Deep Learning

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