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

Uddin, Gias; Baysal, Olga; Guerrouj, Latifa; Khomh, Foutse

doi:10.1109/tse.2019.2903039

Cited by 32 publications

(31 citation statements)

References 87 publications

(241 reference statements)

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“…Such a problem definition adds contextual information over the question title, which may not be enough to explain properly the development task. This assumption is consistent with our previous findings of surveys of software developers who reported the necessity of adding contextual and situationally relevant information into summaries produced from developer forums [89].…”

Section: Work Related To the Three Proposed Algorithmssupporting

confidence: 92%

“…In our previous research of two surveys We also found that developers use such documentation to support diverse development tasks (e.g., bug fixing, API selection, feature usage, etc.) [88]. Therefore, it is necessary that our mining framework is capable of supporting any development scenario.…”

Section: What Is the Performance Of The Algorithm To Link Code Examplmentioning

confidence: 99%

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Mining API usage scenarios from stack overflow

Uddin

Khomh

Roy

2020

Information and Software Technology

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Context: APIs play a central role in software development. The seminal research of Carroll et al. [15] on minimal manual and subsequent studies by Shull et al. [79] showed that developers prefer task-based API documentation instead of traditional hierarchical official documentation (e.g., Javadoc). The Q&A format in Stack Overflow offers developers an interface to ask and answer questions related to their development tasks. Objective: With a view to produce API documentation, we study automated techniques to mine API usage scenarios from Stack Overflow. Method: We propose a framework to mine API usage scenarios from Stack Overflow. Each task consists of a code example, the task description, and the reactions of developers towards the code example. First, we present an algorithm to automatically link a code example in a forum post to an API mentioned in the textual contents of the forum post. Second, we generate a natural language description of the task by summarizing the discussions around the code example. Third, we automatically associate developers reactions (i.e., positive and negative opinions) towards the code example to offer information about code quality. Results: We evaluate the algorithms using three benchmarks. We compared the algorithms against seven baselines. Our algorithms outperformed each baseline. We developed an online tool by automatically mining API usage scenarios from Stack Overflow. A user study of 31 software developers shows that the participants preferred the mined usage scenarios in Opiner over API official documentation. The tool is available online at: http://opiner.polymtl.ca/. Conclusion: With a view to produce API documentation, we propose a framework to automatically mine API usage scenarios from Stack Overflow, supported by three novel algorithms. We evaluated the algorithms against

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Section: Work Related To the Three Proposed Algorithmssupporting

confidence: 92%

Section: What Is the Performance Of The Algorithm To Link Code Examplmentioning

confidence: 99%

Mining API usage scenarios from stack overflow

Uddin

Khomh

Roy

2020

Information and Software Technology

Self Cite

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“…Stack Overflow is a popular software question and answer platform with extensive discussion on APIs as well as a rich amount of API reviews [2]. It becomes a valuable resource and attracts many researchers to further leverage the API information for developers [1], [3].…”

Section: Introductionmentioning

confidence: 99%

“…Previous research found that API documentation often suffers from quality problems, i.e., incomplete, obsolete, and/or incorrect API documentation [4]. Thus, many developers seek help and insights about API from other developers in online developer forums (i.e., Stack Overflow) [2]. Uddin et al [2] surveyed 178 software developers to understand their requirements when searching for API information on Q&A platforms.…”

Section: Introductionmentioning

confidence: 99%

Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?

Yang¹,

Xu²,

Khan³

et al. 2022

Preprint

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APIs (Application Programming Interfaces) are reusable software libraries and are building blocks for modern rapid software development. Previous research shows that programmers frequently share and search for reviews of APIs on the mainstream software question and answer (Q&A) platforms like Stack Overflow, which motivates researchers to design tasks and approaches related to process API reviews automatically. Among these tasks, classifying API reviews into different aspects (e.g., performance or security), which is called the aspect-based API review classification, is of great importance. The current state-of-the-art (SOTA) solution to this task is based on the traditional machine learning algorithm. Inspired by the great success achieved by pre-trained models on many software engineering tasks, this study fine-tunes six pre-trained models for the aspect-based API review classification task and compares them with the current SOTA solution on an API review benchmark collected by Uddin et al. The investigated models include four models (BERT, RoBERTa, ALBERT and XLNet) that are pretrained on natural languages, BERTOverflow that is pre-trained on text corpus extracted from posts on Stack Overflow, and CosSensBERT that is designed for handling imbalanced data. The results show that all the six fine-tuned models outperform the traditional machine learning-based tool. More specifically, the improvement on the F1-score ranges from 21.0% to 30.2%. We also find that BERTOverflow, a model pre-trained on the corpus from Stack Overflow, does not show better performance than BERT. The result also suggests that CosSensBERT also does not exhibit better performance than BERT in terms of F1, but it is still worthy of being considered as it achieves better performance on MCC and AUC.

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“…However, it is not always clear how to compare and select the best library from a seemingly similar set of available libraries. Thus, developers often spend time to understand differences between available libraries by, for example, reading the libraries' documentation or searching for blogs or posts on Stack Overflow [17,22]. To save developers' time and address their need to compare libraries, we previously proposed the idea of metric-based library comparisons [7,8] where we compiled a set of metrics that can be used to compare libraries.…”

mentioning

confidence: 99%

LibComp: an IntelliJ plugin for comparing Java libraries

El-Hajj

Nadi

2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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Software developers heavily rely on third-party libraries to accomplish their programming tasks. Since many libraries offer similar functionality, it can be difficult and tedious for developers differentiate similar libraries in order to select the most suitable one. In our previous work, we proposed the idea of metric-based library comparisons that allow developers to compare various aspects of libraries within the same domain, empowering them with information to aid with their decision. In this paper we present an IntelliJ plugin, LibComp, that provides this library metric-based comparison technique right within the developer's IDE. As soon as a developer adds a library dependency that LibComp has information about, LibComp will highlight this dependency to let the developer know that there are alternatives available. Once the user triggers the comparison for that library, they can view various metrics about the library and its alternatives and decide if they want to use one of the alternatives. In the process, LibComp also records the number of times the developer invokes the tool and any completed replacements. Such feedback, if optionally sent to us by the developer, provides us valuable insights into developers' replacement decisions as well as information on how we can improve the tool. A video demonstrating the usage of LibComp can be found at https://youtu.be/YtEEdJan77A CCS CONCEPTS • Software and its engineering → Software libraries and repositories; Software maintenance tools.

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

Cited by 32 publications

References 87 publications

Mining API usage scenarios from stack overflow

Mining API usage scenarios from stack overflow

Aspect-Based API Review Classification: How Far Can Pre-Trained Transformer Model Go?

LibComp: an IntelliJ plugin for comparing Java libraries

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