What is asked about technical debt (TD) on Stack Exchange question-and-answer (Q&amp;A) websites? An observational study

Alfayez, Reem; Ding, Yunyan; Winn, Robert; Alfayez, Ghaida; Harman, Christopher; Boehm, Barry

doi:10.1007/s10664-022-10269-5

Cited by 6 publications

(9 citation statements)

References 52 publications

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“…li et al (Li et al, 2021) provide a multi-platform perspective on quantum software engineering by combining Stack Exchange and GitHub Issues data and identified 9 topics through stack exchange data. Other studies have manually classified the topics addressed within question and answer platforms focusing on MATLAB (Naghashzadeh et al, 2021), Technical Debt (Alfayez et al, 2023), and discussions related to code smells and anti-patterns (Tahir et al, 2020).…”

Section: Topic Analysis In Qanda Platformsmentioning

confidence: 99%

“…Determine tags associated with XAI. In the subsequent phase, we employed a widely recognized approach, previously used in numerous studies (Li et al, 2021;Alfayez et al, 2023;Abdellatif et al, 2020;Uddin et al, 2021), to expand our collection of tags. Initially, we utilized the tags acquired earlier, along with "explainable-ai" and "xai".…”

Section: #T18mentioning

confidence: 99%

“…Each question must be accompanied by at least one tag and can have a maximum of five tags. Previous research has demonstrated that analyzing these tags can offer additional context to the field, shedding light on the specific topics and related categories discussed (Alfayez et al, 2023). In this section, our initial step involves determining the frequency of each tag's usage in the posts of the XAI dataset within each forum.…”

Section: Approachmentioning

confidence: 99%

“…In line with the guidance in section 4.1, we manually annotated topics to ensure optimal outcomes, steering clear of less-than-ideal results (De Lucia et al, 2014). To enhance the granularity of LDA findings, we took inspiration from previous studies (Wang et al, 2023;Alfayez et al, 2023;Naghashzadeh et al, 2021), where discussions were manually categorized. Employing a reliable method and adhering to standards established in prior research, we systematically categorized the topics and categorized each of them by different subtopics within this study.…”

Section: Topic Analysismentioning

confidence: 99%

“…Each question within the Stack Exchange forums is associated with tags representing the high-level categories of the questions. Analyzing these tags can offer context to specific topics (Alfayez et al, 2023). We first determine tag frequencies in the XAI dataset posts within each forum.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Black Box: Analyzing Explainable AI Challenges and Best Practices Through Stack Exchange Discussions

Sayyadnejad,

Asgari,

Sami

et al. 2024

Preprint

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Explainable Artificial Intelligence (XAI) is a crucial domain within research and industry, aiming to develop AI models that provide human-understandable explanations for their decisions. While the challenges in AI, deep learning, and big data have been extensively explored, the specific concerns of XAI developers have received limited attention. To address this gap, we analyzed discussions on Stack Exchange websites to delve into these issues. Through a combination of automated and manual analysis, we identified 6 overarching categories, 10 distinct topics, and 40 sub-topics commonly discussed by developers. Our examination revealed a steady rise in discussions on XAI since late 2015, initially focusing on conceptualization and practical applications, with a notable surge in activity across all topic categories since 2019. Notably, Concepts and Applications, Tools Troubleshooting, and Neural Networks Interpretation emerged as the most popular topics. Troubleshooting challenges were commonly encountered with tools like Shap, Eli5, and Aif360, while Visualization issues were prevalent with Yellowbrick and Shap. Furthermore, our analysis suggests that addressing questions related to XAI poses greater difficulty compared to other machine-learning questions.

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Section: Topic Analysis In Qanda Platformsmentioning

confidence: 99%

Section: #T18mentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Section: Topic Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploring the Black Box: Analyzing Explainable AI Challenges and Best Practices Through Stack Exchange Discussions

Sayyadnejad,

Asgari,

Sami

et al. 2024

Preprint

View full text Add to dashboard Cite

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An Observational Study on React Native (RN) Questions on Stack Overflow (SO)

Albesher,

Aldossari,

Alfayez

2023

IET Software

Self Cite

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Mobile applications are continuously increasing in prevalence. One of the main challenges in mobile application development is creating cross-platform applications. To facilitate developing cross-platform applications, the software engineering community created several solutions, one of which is React Native (RN), which is a popular cross-platform framework. The software engineering literature demonstrated the effectiveness of Stack Overflow (SO) in providing real-world perspectives on a variety of technical subjects. Therefore, this study aims to gain a better understanding of the stance of RN on SO. We identified and analyzed 131,620 SO RN-related questions. Moreover, we observed how the interest toward RN on SO evolves over time. Additionally, we utilized Latent Dirichlet Allocation (LDA) to identify RN-related topics that are discussed within the questions. Afterward, we utilized a number of proxy measures to estimate the popularity and difficulty of these topics. The results revealed that interest toward RN on SO was generally increasing. Moreover, RN-related questions revolve around six topics, with the topics of layout and navigation being the most popular and the topic of iOS issues being the most difficult. Software engineering researchers, practitioners, educators, and RN contributors may find the results of this study beneficial in guiding their future RN efforts.

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An Observational Study on Flask Web Framework Questions on Stack Overflow (SO)

Albesher,

Alfayez

2024

IET Software

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Web‐based applications are popular in demand and usage. To facilitate the development of web‐based applications, the software engineering community developed multiple web application frameworks, one of which is Flask. Flask is a popular web framework that allows developers to speed up and scale the development of web applications. A review of the software engineering literature revealed that the Stack Overflow (SO) website has proven its effectiveness in providing a better understanding of multiple subjects within the software engineering field. This study aims to analyze SO Flask‐related questions to gain a better understanding of the stance of Flask on the website. We identified a set of 70,230 Flask‐related questions that we further analyzed to estimate how the interest towards the framework evolved over time on the website. Afterward, we utilized the Latent Dirichlet Allocation (LDA) algorithm to identify Flask‐related topics that are discussed within the set of the identified questions. Moreover, we leveraged a number of proxy measures to examine the difficulty and popularity of the identified topics. The study found that the interest towards Flask has been generally increasing on the website, with a peak in 2020 and drops in the following years. Moreover, Flask‐related questions on SO revolve around 12 topics, where Application Programming Interface (API) can be considered the most popular topic and background tasks can be considered the most difficult one. Software engineering researchers, practitioners, educators, and Flask contributors may find this study useful in guiding their future Flask‐related endeavors.

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What is asked about technical debt (TD) on Stack Exchange question-and-answer (Q&A) websites? An observational study

Cited by 6 publications

References 52 publications

Exploring the Black Box: Analyzing Explainable AI Challenges and Best Practices Through Stack Exchange Discussions

Exploring the Black Box: Analyzing Explainable AI Challenges and Best Practices Through Stack Exchange Discussions

An Observational Study on React Native (RN) Questions on Stack Overflow (SO)

An Observational Study on Flask Web Framework Questions on Stack Overflow (SO)

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