Augmenting stack overflow with API usage patterns mined from GitHub

Reinhardt, Anastasia; Zhang, Tianyi; Mathur, Mihir; Kim, Miryung

doi:10.1145/3236024.3264585

Cited by 15 publications

(4 citation statements)

References 10 publications

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“…• Realize not everyone posting an answer is your friend-there may be unintentional or malicious poor code posted. Action for users: utilize plugins warning of dangerous code [51], [52].…”

Section: Guidelines For Reading and Using Stack Overflowmentioning

confidence: 99%

The Impact of Surface Features on Choice of (in)Secure Answers by Stackoverflow Readers

Linden

Williams

Hallett

et al. 2022

IIEEE Trans. Software Eng.

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Existing research has shown that developers will use StackOverflow to answer programming questions: but what draws them to one particular answer over any other? The choice of answer they select can mean the difference between a secure application and insecure one, as the quality of supposedly secure answers can vary. Prior work has studied people posting on Stack Overflow-a two-way communication between the original poster and the Stack Overflow community. Instead, we study the situation of one-way communication, where people only read a Stack Overflow thread without being actively involved in it, sometimes long after a thread has closed. We report on a mixed-method study including a controlled between-groups experiment and qualitative analysis of participants' rationale (N=1188), investigating whether explanation detail, answer scoring, accepted answer marks, as well as the security of the code snippet itself affect the answers participants accept. Our findings indicate that explanation detail affects what answers participants reading a thread select (p<0.01), while answer score and acceptance do not (p>0.05)-the inverse of what research has shown for those asking and answering questions. The qualitative analysis of participants' rationale further explains how several cognitive biases underpin these findings. Correspondence bias, in particular, plays an important role in instilling readers with a false sense of confidence in an answer through the way it looks, regardless of whether it works, is secure, or if the community agrees with it. As a result, we argue that StackOverflow's use as a knowledge base by people not actively involved in threads-when there is only one-way-communication-may inadvertently contribute to the spread of insecure code, as the community's voting mechanisms hold little power to deter them from answers.

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“…• Realize not everyone posting an answer is your friend-there may be unintentional or malicious poor code posted. Action for users: utilize plugins warning of dangerous code [51], [52].…”

Section: Guidelines For Reading and Using Stack Overflowmentioning

confidence: 99%

The Impact of Surface Features on Choice of (in)Secure Answers by Stackoverflow Readers

Linden

Williams

Hallett

et al. 2022

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

show abstract

“…There are other research efforts on other ways to assist development using APIs. Researchers have worked on better search for API usage examples [50], [90], [91], recommendation of APIs [92], [93], [94], generating or improving documentation [95], [96], studying API usage in StackOverflow answers [79], [97], and finding API workarounds [36]. Lamothe et al [36] showed that developers may intentionally use APIs in ways that are not officially supported by the API developers; these workarounds may be viewed as alternative usage patterns.…”

Section: Other Work On Apimentioning

confidence: 99%

Active Learning of Discriminative Subgraph Patterns for API Misuse Detection

Kang,

2022

Preprint

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A common cause of bugs and vulnerabilities are the violations of usage constraints associated with Application Programming Interfaces (APIs). API misuses are common in software projects, and while there have been techniques proposed to detect such misuses, studies have shown that they fail to reliably detect misuses while reporting many false positives. One limitation of prior work is the inability to reliably identify correct patterns of usage. Many approaches confuse a usage pattern's frequency for correctness. Due to the variety of alternative usage patterns that may be uncommon but correct, anomaly detection-based techniques have limited success in identifying misuses. We address these challenges and propose ALP (Actively Learned Patterns), reformulating API misuse detection as a classification problem. After representing programs as graphs, ALP mines discriminative subgraphs. While still incorporating frequency information, through limited human supervision, we reduce the reliance on the assumption relating frequency and correctness. The principles of active learning are incorporated to shift human attention away from the most frequent patterns. Instead, ALP samples informative and representative examples while minimizing labeling effort. In our empirical evaluation, ALP substantially outperforms prior approaches on both MUBench, an API Misuse benchmark, and a new dataset that we constructed from real-world software projects.

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“…[11,12]), and API recommendation (e.g. [10,15]) can benefit from the code embeddings used in our study.…”

Section: Introductionmentioning

confidence: 99%

Code2Que: A Tool for Improving Question Titles from Mined Code Snippets in Stack Overflow

Gao

Xia

et al. 2020

Preprint

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Stack Overflow is one of the most popular technical Q&A sites used by software developers. Seeking help from Stack Overflow has become an essential part of software developers' daily work for solving programming-related questions. Although the Stack Overflow community has provided quality assurance guidelines to help users write better questions, we observed that a significant number of questions submitted to Stack Overflow are of low quality. In this paper, we introduce a new web-based tool, Code2Que, which can help developers in writing higher quality questions for a given code snippet. Code2Que consists of two main stages: offline learning and online recommendation. In the offline learning phase, we first collect a set of good quality ⟨code snippet, question⟩ pairs as training samples. We then train our model on these training samples via a deep sequence-to-sequence approach, enhanced with an attention mechanism, a copy mechanism and a coverage mechanism. In the online recommendation phase, for a given code snippet, we use the offline trained model to generate question titles to assist less experienced developers in writing questions more effectively. At the same time, we embed the given code snippet into a vector and retrieve the related questions with similar problematic code snippets. To evaluate Code2Que, we first sampled 50 low quality ⟨code snippet, question⟩ pairs from the Python and Java datasets on Stack Overflow. Then we conducted a user study to evaluate the question titles generated by our approach as compared to human-written ones using three metrics: Clearness, Fitness and Willingness to Respond. Our experimental results show that for a large number of low-quality questions in Stack Overflow, Code2Que can improve the question titles in terms of Clearness, Fitness and Willingness measures. Code2Que can be accessed at http://www.code2que.com. A demo video of Code2Que is at https://youtu.be/orG--uXKnkU.

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Augmenting stack overflow with API usage patterns mined from GitHub

Cited by 15 publications

References 10 publications

The Impact of Surface Features on Choice of (in)Secure Answers by Stackoverflow Readers

The Impact of Surface Features on Choice of (in)Secure Answers by Stackoverflow Readers

Active Learning of Discriminative Subgraph Patterns for API Misuse Detection

Code2Que: A Tool for Improving Question Titles from Mined Code Snippets in Stack Overflow

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