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Bug Analysis in Jupyter Notebook Projects: An Empirical Study

ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 2024年第4期33卷 1-34页

作者： De Santana, Taijara Loiola Da Mmota Silveira Neto, Paulo Anselmo De Almeida, Eduardo Santana Ahmed, Iftekhar Univ Fed Bahia Inst Comp IC UFBA Rua Augusto Viana S-N Palacio Reitoria BR-40110909 Salvador BA Brazil Fed Rural Univ Pernambuco UFRPE Rua Dom Manuel Medeiros S-N BR-52171900 Recife PE Brazil Univ Calif Irvine Irvine CA 92697 USA

Computational notebooks, such as Jupyter, have been widely adopted by data scientists to write code for analyzing and visualizing data. Despite their growing adoption and popularity, few studies have been found to understand Jupyter development challenges from the practitioners' point of view. This article presents a systematic study of bugs and challenges that Jupyter practitioners face through a large-scale empirical investigation. We mined 14,740 commits from 105 GitHub open source projects with Jupyter Notebook code. Next, we analyzed 30,416 stackoverflow posts, which gave us insights into bugs that practitioners face when developing Jupyter Notebook projects. Next, we conducted 19 interviews with data scientists to uncover more details about Jupyter bugs and to gain insight into Jupyter developers' challenges. Finally, to validate the study results and proposed taxonomy, we conducted a survey with 91 data scientists. We highlight bug categories, their root causes, and the challenges that Jupyter practitioners face.

关键词： Jupyter Notebooks bugs interviews mining software repositories (MSR) stackoverflow empirical study

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Extracting and Summarizing the Commonly Faced Security Issues from Community Question Answering Site

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INTERNATIONAL JOURNAL OF INFORMATION SECURITY AND PRIVACY 2019年第3期13卷 48-59页

作者： Singh, Abhishek Kumar Nagwani, Naresh Kumar Pandey, Sudhakar NIT Raipur Raipur Madhya Pradesh India Natl Inst Technol Raipur Raipur Madhya Pradesh India

Community question-answering (CQA) sites are popular as information-seeking platforms where users communicate to their peers. Security-related posts are gaining popularity with the rapid development of information technology in these sites and. CQA sites contains wide range of posts from classic cryptography to recently popular mobile security. Investigating such posts can be useful for researchers, teachers and developers. In this article, spectral clustering and frequent term-based summarization techniques are proposed for security related posts. The proposed method is developed in three stages. In the first stage, security related folksonomies are created and security post profile matrix is built with the help of tag frequency-inverse security post frequency. In the second stage, security related posts are grouped with help of spectral clustering algorithms. Finally, in the third stage, frequent terms are extracted from each cluster for security related post summarization with the help of frequent words and semantic similarity.

关键词： Community Question Answering Sites Frequent Words Security Semantic Similar stackoverflow Text Summarization

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Expert ranking techniques for online rated forums

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COMPUTERS IN HUMAN BEHAVIOR 2019年 100卷 168-176页

作者： Faisal, Muhammad Shahzad Daud, Ali Akram, Abubakr Usman Abbasi, Rabeeh Ayaz Aljohani, Naif Radi Mehmood, Irfan Int Islamic Univ Dept Comp Sci & Software Islamabad Pakistan COMSATS Inst IT Dept Comp Sci Attock Pakistan KAU FCIT Dept Informat Syst Jeddah Saudi Arabia Quaid I Azam Univ Dept Comp Sci Islamabad Pakistan Sejong Univ Dept Software Seoul South Korea

Web 2.0 or social web applications such as online discussion forums, blogs and Wikipedia have improved knowledge sharing by providing an environment in which users can generate and find their favorite content in, a flexible way. With the passage of time, online discussion forums accumulate a huge amount of content and this can introduce issues of content quality and user credibility. A poor-quality answer in a discussion forum indicates the presence of unprofessional or unqualified users;therefore, a priority is to find experts or reputable users. Most of the existing expert-ranking approaches consider basic features, such as the total number of answers provided by a user, but ignore the quality and consistency of the user's answer. In this paper, expert-ranking techniques using g-index are proposed, and are applied to a stackoverflow forum dataset. Three techniques are proposed including Exp-PC, Rep-FS and Weighted Exp-PC. Exp-PC is an adaptation of g-index for ranking experts in stackoverflow forum. In Rep-FS, several features like voters reputation, vote ratio are proposed to measure users' expertise while Weighted Exp-PC computes user expertise by combining their Exp-PC and Rep-FS scores. We measure users' reputation and expertise according to both the quality of their answer and their consistency in providing quality answers. The experimental results of the proposed expert-ranking techniques, Exp-PC and Weighted Exp-PC in particular, validate that these methods identify genuine experts in a more effective way.

关键词： Expert finding Online rated forums stackoverflow Information retrieval Social media

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Automated Error Log Resolution: A Case Study 21

Automated Error Log Resolution: A Case Study

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36th Annual ACM Symposium on Applied Computing (SAC)

作者： Fuller, Mark Brighton, Elizabeth Schiewe, Micah Das, Dipta Cerny, Tomas Tisnovsky, Pavel Baylor Univ Waco TX 76798 USA Red Hat Brno Czech Republic

ISBN: (纸本)9781450381048

Debugging and error resolution has become increasingly time-consuming and difficult for all domains of software development. Error logs have become very important when it comes to debugging and error resolution. To remedy the problems presented in the logs, typically, a search on online forums would shed light on the solution. We present a novel approach to utilizing these logs in conjunction with external Question and Answer forums to compute and expedite resolution by suggesting a solution to runtime errors. Since log format is non-standard and use cases can vary widely, our architecture allows for extreme customization for the intended ecosystem as well as a great degree of fine-tuning. We evaluated our solution in a case study and made our implementation open-source for the community.

关键词： Defect Resolution Error Logs Log Analysis stackoverflow

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On the Dynamics of Topic-Based Communities in Online Knowledge-Sharing Networks 2

On the Dynamics of Topic-Based Communities in Online Knowled...

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2nd European Network Intelligence Conference (ENIC)

作者： Guimaraes, Anna Couto da Silva, Ana Paula Almeida, Jussara M. Univ Fed Minas Gerais Dept Comp Sci Belo Horizonte MG Brazil

ISBN: (纸本)9781467375924

Online knowledge-sharing networks, such as wikis and Question-Answering (Q&A) portals, offer a rich environment where people can collaborate and share knowledge on specific topics pertaining to their interests and expertise. The continued collaboration of groups of users who share interests in the same topics essentially make up communities, which are here referred to as topic-based communities. Analyzing the dynamics of such communities is key to understanding network processes such as the flow of users and information in the network. In this paper, we study how users relate to topic-based communities and how such relationship shapes long-term community dynamics. Using a large dataset collected from Stack Overflow, a popular programming oriented Q&A site, we investigate several factors related to the evolution of communities in the site, such as user participation and the importance of revisits for community sustainability. Moreover, we develop a model to describe community evolution based on user activity which incorporates key aspects of community dynamics, including member revisits and the flow of users across related communities.

关键词： portals social networking (online) Q&A portals stackoverflow Wikis community evolution long-term community dynamics online knowledge-sharing networks programming oriented Q&A site question-answering portals topic-based community dynamics Collaboration Databases Electric shock Information services Knowledge engineering Programming Rails community dynamics epidemic model knowledge sharing question-answering systems topic-based communities community dynamics Portals websites Electric Injuries Electroconvulsive Shock Knowledge based engineering Information Services Rail Railway Rail knowledge sharing Programming Wikipedia Wikis Consumers Database

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Understanding and Classifying the Quality of Technical Forum Questions 14

Understanding and Classifying the Quality of Technical Forum...

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14th Annual International Conference on Quality Software (QSIC)

作者： Ponzanelli, Luca Mocci, Andrea Bacchelli, Alberto Lanza, Michele Univ Lugano Fac Informat REVEAL Lugano Switzerland Delft Univ Technol NL-2600 AA Delft Netherlands

ISBN: (纸本)9781479971978

Technical questions and answers (Q&A) services have become a valuable resource for developers. A prominent example of technical Q&A website is Stack Overflow (SO), which relies on a growing community of more than two millions of users who actively contribute by asking questions and providing answers. To maintain the value of this resource, poor quality questions-among the more than 6,000 asked daily-have to be filtered out. Currently, poor quality questions are manually identified and reviewed by selected users in SO;this costs considerable time and effort. Automating the process would save time and unload the review queue, improving the efficiency of SO as a resource for developers. We present an approach to automate the classification of questions according to their quality. We present an empirical study that investigates how to model and predict the quality of a question by considering as features both the contents of a post (e.g., from simple textual features to more complex readability metrics) and community-related aspects (e.g., popularity of a user in the community). Our findings show that there is indeed the possibility of at least a partial automation of the costly SO review process.

关键词： Web sites question answering (information retrieval) Q & A Web site SO stackoverflow questions and answers services technical forum questions Communities Decision trees Entropy Feature extraction Indexes Measurement Predictive models Q& A Quality Technical Forum websites Decision Trees Prediction models Feature extraction entropy Automation Developers Consumers valuable resource

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Skill Translation Models in Expert Finding 17

Skill Translation Models in Expert Finding

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40th International ACM SIGIR conference on research and development in Information Retrieval

作者： Nobari, Arash Dargahi Gharebagh, Sajad Sotudeh Neshati, Mahmood Shahid Beheshti Univ Fac Comp Sci & Engn Tehran Iran

ISBN: (纸本)9781450350228

Finding talented users on stackoverflow can be a challenging task due to term mismatch between queries and content published on it. In this paper, we propose two translation models to augment a given query with relevant words. The first model is based on a statistical approach and the second one is a word embedding model. Interestingly, the translations provided by these methods are not the same. Although the first model in most cases selects pieces of program codes as translations, the second model provides more semantically related words. Our experiments on a large dataset indicate the efficiency of proposed models.

关键词： Expertise retrieval Statistical Machine Translation Semantic matching stackoverflow Talent acquisition

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A Qualitative Investigation of Insecure Code Propagation from Online Forums

A Qualitative Investigation of Insecure Code Propagation fro...

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IEEE Secure Development Conference (IEEE SecDev)

作者： Bai, Wei Akgul, Omer Mazurek, Michelle L. Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9781538672891

Research demonstrates that code snippets listed on programming-oriented online forums (e.g., Stack Overflow) - including snippets containing security mistakes - make their way into production code. Prior work also shows that software developers who reference Stack Overflow in their development cycle produce less secure code. While there are many plausible explanations for why developers propagate insecure code in this manner, there is little or no empirical evidence. To address this question, we identify Stack Overflow code snippets that contain security errors and find clones of these snippets in open source GitHub repositories. We then survey (n=133) and interview (n=15) the authors of these GitHub repositories to explore how and why these errors were introduced. We find that some developers (perhaps mistakenly) trust their security skills to validate the code they import, but the majority admit they would need to learn more about security before they could properly perform such validation. Further, although some prioritize functionality over security, others believe that ensuring security is not, or should not be, their responsibility. Our results have implications for attempts to ameliorate the propagation of this insecure code.

关键词： Code reuse stackoverflow Vulnerability propagation

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Automatically augmenting learning material with practical questions to increase its relevance 45

Automatically augmenting learning material with practical qu...

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45th Annual Frontiers in Education Conference (FIE)

作者： Singh, Gaurav Kumar Kumar, Varun Bhat, Savita Pedanekar, Niranjan Tata Consultancy Serv Ltd Syst Res Lab Tata Res Dev & Design Ctr Pune 411013 Maharashtra India

ISBN: (纸本)9781479984541

Relevance of a concept being taught to the real world is believed to contribute to an increase in the intrinsic motivation and engagement of a learner. Such relevance is often found lacking in learning material such as textbooks. Practical issues and problems one could face while learning or implementing new concepts are a means of establishing such relevance. In this paper, we propose a method to automatically augment learning material with practical questions about the concept being learnt. We use questions and answers from stackoverflow, a leading social Questions and Answers (Q&A) website to augment an electronic textbook interface, thus connecting the concepts being taught to the real world. For achieving this automatically, we first mine the textbook content to locate words and phrases which are likely to be the most important concepts on each page of the textbook. We then select only those words and phrases which appear as 'tags' in stackoverflow, typically defined by users while asking and answering questions. Using permutations of these tags as queries, we query the stackoverflow database to obtain relevant questions and answers to augment any given page. We present an interface to augment textbooks with such questions using the aforementioned method. We also present the results of a student survey examining the effectiveness of the augmentation in establishing relevance to their learning.

关键词： Computer Science Education e-learning Relevance Questions stackoverflow Augmentation

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Data Driven Identification of Factors Affecting the Job Satisfaction of Programmers 8

Data Driven Identification of Factors Affecting the Job Sati...

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8th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Teimouri, Shohreh Amirkhani, Hossein Univ Qom Dept Informat Technol & Comp Engn Qom Iran

ISBN: (纸本)9781538695692

In this paper, the factors affecting the programmers' job satisfaction are investigated. We use the recent questionnaire by the stackoverflow team containing 153 questions with more than 50,000 samples from 213 different countries. After some preprocessing, a dataset containing 40,377 samples with 77 factors is prepared. The importance of different factors on job satisfaction are investigated using five different methods including random forest, linear regression, light GBM, correlation analysis, and a hand-designed variance based technique. Finally, different ordered lists obtained by these methods are aggregated using the Borda count algorithm. The results show that factors like career satisfaction, job seeking status, developer type, hours per week to find a new job, and salary are among the most influential factors on programmers' job satisfaction.

关键词： Job Satisfaction Programmer Borda Count stackoverflow

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