Selection and presentation practices for code example summarization

Ying, Annie T. T.; Robillard, Martin P.

doi:10.1145/2635868.2635877

Cited by 28 publications

(9 citation statements)

References 43 publications

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“…Developers in our surveys wish for tools to automatically summarize the huge volume of opinions posted about APIs from developer forums. Such summaries can be different from the research conducted to produce Natural language and structural summaries of source code [21], [39], [40], [41], [42], [43], [44], [45], [46], [74], [75], [76].…”

Section: Summarization Of Software Artifactsmentioning

confidence: 99%

Understanding How and Why Developers Seek and Analyze API-Related Opinions

Uddin

Baysal

Guerrouj

et al. 2021

IIEEE Trans. Software Eng.

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With the advent and proliferation of online developer forums as informal documentation, developers often share their opinions about the APIs they use. Thus, opinions of others often shape the developer's perception and decisions related to software development. For example, the choice of an API or how to reuse the functionality the API offers are, to a considerable degree, conditioned upon what other developers think about the API. While many developers refer to and rely on such opinion-rich information about APIs, we found little research that investigates the use and benefits of public opinions. To understand how developers seek and evaluate API opinions, we conducted two surveys involving a total of 178 software developers. We analyzed the data in two dimensions, each corresponding to specific needs related to API reviews: (1) Needs for seeking API reviews, and (2) Needs for automated tool support to assess the reviews. We observed that developers seek API reviews and often have to summarize those for diverse development needs (e.g., API suitability). Developers also make conscious efforts to judge the trustworthiness of the provided opinions and believe that automated tool support for API reviews analysis can assist in diverse development scenarios, including, for example, saving time in API selection as well as making informed decisions on a particular API features.

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Section: Summarization Of Software Artifactsmentioning

confidence: 99%

Understanding How and Why Developers Seek and Analyze API-Related Opinions

Uddin

Baysal

Guerrouj

et al. 2021

IIEEE Trans. Software Eng.

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“…The inclusion of thread-related features could have improvements even in recall. The query "Generate random integer" has 48 answers on goldset, and 28 of them are from the same thread 4 . It is a popular thread with several answers with high score, but CROKAGE had recommended an answer from this thread only in the 98 th position.…”

Section: State-of-the-art Limitationsmentioning

confidence: 99%

“…Developers often search for solutions on the web for their programming tasks in the form of code examples and their explanations [1], spending up to 20% of their time in this activity [2,3]. Although code search engines (e.g., GitHub, Google Code Search) can be used for such a task, finding these solutions for programming tasks is still a challenge because not all code examples returned for a query have the same quality [4]. Developers need to carefully choose appropriate keywords that represent their programming task (i.e., the query) due to the semantic gap existent between the query and the appropriate code.…”

Section: Introductionmentioning

confidence: 99%

Improved Retrieval of Programming Solutions With Code Examples Using a Multi-featured Score

Silva¹,

Rahman²,

Dantas³

et al. 2021

Preprint

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Developers often depend on code search engines to obtain solutions for their programming tasks. However, finding an expected solution containing code examples along with their explanations is challenging due to several issues. There is a vocabulary mismatch between the search keywords (the query) and the appropriate solutions. Semantic gap may increase for similar bag of words due to antonyms and negation. Moreover, documents retrieved by search engines might not contain solutions containing both code examples and their explanations. So, we propose CRAR (Crowd Answer Recommender) to circumvent those issues aiming at improving retrieval of relevant answers from Stack Overflow containing not only the expected code examples for the given task but also their explanations. Given a programming task, we investigate the effectiveness of combining information retrieval techniques along with a set of features to enhance the ranking of important threads (i.e., the units containing questions along with their answers) for the given task and then selects relevant answers contained in those threads, including semantic features, like word embeddings and sentence embeddings, for instance, a Convolutional Neural Network (CNN). CRAR also leverages social aspects of Stack Overflow discussions like popularity to select relevant answers for the tasks. Our experimental evaluation shows that the combination of the different features performs better than each one individually. We also compare the retrieval performance with the state-of-art CROK-AGE (Crowd Knowledge Answer Generator), which is also a system aimed at retrieving relevant answers from Stack Overflow. We show that CRAR outperforms CROKAGE in Mean Reciprocal Rank and Mean Recall with small and medium effect sizes, respectively.

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“…This raises an interesting question: Does this level of disagreement occur because the PAM patterns are not representative of the client code they were mined from, or because the hand-written examples themselves are not fully representative of the client code? Although previous work has explored what it means for a single API example to be useful [42,34], there seems to be much less work about what it means for a set of examples to be useful, and how well example directories in popular projects reflect all of the actual common uses of an API. We can however make an initial assessment of this question by going back to the held-out test set of client code that we used in RQ1.…”

Section: Rq3: Could the Api Patterns Mined By Pam Supplement Existingmentioning

confidence: 99%

Parameter-free probabilistic API mining across GitHub

Fowkes

Sutton

2016

Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering

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Existing API mining algorithms can be difficult to use as they require expensive parameter tuning and the returned set of API calls can be large, highly redundant and difficult to understand. To address this, we present PAM (Probabilistic API Miner), a near parameter-free probabilistic algorithm for mining the most interesting API call patterns. We show that PAM significantly outperforms both MAPO and UPMiner, achieving 69% test-set precision, at retrieving relevant API call sequences from GitHub. Moreover, we focus on libraries for which the developers have explicitly provided code examples, yielding over 300,000 LOC of hand-written API example code from the 967 client projects in the data set. This evaluation suggests that the hand-written examples actually have limited coverage of real API usages.

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Selection and presentation practices for code example summarization

Cited by 28 publications

References 43 publications

Understanding How and Why Developers Seek and Analyze API-Related Opinions

Understanding How and Why Developers Seek and Analyze API-Related Opinions

Improved Retrieval of Programming Solutions With Code Examples Using a Multi-featured Score

Parameter-free probabilistic API mining across GitHub

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