DOCSLIB.ORG

The Curious Case of Posts on Stack Overflow

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Curious Case of Posts on Stack Overflow The curious case of posts on Stack Overflow Shailja Shukla Subject: (Information Systems) Corresponds to: (30 hp) Presented: (VT 2020) Supervisor: Mudassir Imran Mustafa Department of Informatics and Media 1 Contents Abstract ...................................................................................................................................... 6 Acknowledgements .................................................................................................................... 7 Chapter 1 .................................................................................................................................... 8 1. Introduction ........................................................................................................................ 8 1.1. Background ................................................................................................................ 8 1.2. Motivation ................................................................................................................ 10 1.2 Research Questions .................................................................................................. 11 1.3 Delimitation: ............................................................................................................ 12 1.4 Limitation:................................................................................................................ 12 Chapter 2 .................................................................................................................................. 13 2. Theory ............................................................................................................................... 13 2.1 Topic Modelling: ..................................................................................................... 13 2.2 Latent Dirichlet Allocation (LDA): ......................................................................... 14 2.3 Related Work ........................................................................................................... 15 Chapter 3 .................................................................................................................................. 17 3. Methodology:.................................................................................................................... 17 3.1 Data Collection: ....................................................................................................... 18 3.2 Data Extraction: ....................................................................................................... 18 3.2.1 Schema: ................................................................................................................. 19 3.3 Data Pre-processing: ................................................................................................ 20 3.1.1 Subset corpus data: .............................................................................................. 20 3.1.2 Remove code snippets: ........................................................................................ 21 3.3.3 Combine related documents to form a single corpus: .......................................... 22 3.3.4 Tokenization: ....................................................................................................... 22 3.3.5 Lowercasing: ........................................................................................................ 23 3.3.6 Remove punctuations: .......................................................................................... 23 3.3.7 Text Standardization/Replace Contractions:........................................................ 23 3.3.8 Remove stop words: ............................................................................................. 24 3.3.9 Remove URLs:..................................................................................................... 24 3.3.10 Minimum size words: ...................................................................................... 24 3.3.11 Remove multiple whitespaces: ........................................................................ 25 3.3.12 Generate N-Grams: .......................................................................................... 25 3.3.13 Stemming: ........................................................................................................ 25 3.3.14 Lemmatisation: ................................................................................................ 26 2 3.4 Create Dictionary and Term Document Frequency: ................................................ 26 3.5 Run the LDA model: ................................................................................................ 28 Chapter 4 .................................................................................................................................. 29 4 Analysis: ........................................................................................................................... 29 Chapter 5 .................................................................................................................................. 34 5 Result ................................................................................................................................ 34 5.1 RQ1- What are the popular discussion topics in Stack Overflow? .......................... 34 5.1.1 Web as a recurring discussion topic: ................................................................... 36 5.1.2 UI Development as a recurring discussion topic: ................................................ 37 5.1.3 Data management as a recurring discussion topic: .............................................. 37 5.2 RQ2- How does the developer's interest change over time? ................................... 38 5.3 RQ3- How do the interests in specific technologies change over time?.................. 39 5.3.1 React vs Angular .................................................................................................. 39 5.3.2 Python vs JavaScript ............................................................................................ 40 5.3.3. Popular discussion topics related to Web technologies ................................... 40 5.3.4 Relational Databases (RDBMS) .......................................................................... 41 5.3.5 Android vs iOS .................................................................................................... 42 5.3.6 Object-Oriented Programming............................................................................. 43 5.3.7 Machine Learning ................................................................................................ 44 Chapter 6 .................................................................................................................................. 45 6 Validity of research and experiences: ............................................................................... 45 Chapter 7 .................................................................................................................................. 46 7 Conclusion: ....................................................................................................................... 46 Chapter 8 .................................................................................................................................. 47 8 Discussion & Future Work: .............................................................................................. 47 Appendix 1: Tools and technology .......................................................................................... 48 Appendix 2: Popular discussion topics lists among developers: ............................................. 49 Appendix 3: Acronym / Abbreviation Table ........................................................................... 54 References: ............................................................................................................................... 56 3 Table of Figures: Figure 1: Venn Diagram of the intersection of the Text Mining and six related fields (Miner et al., 2012) ................................................................................................................................ 9 Figure 2: Schematic Overview of LDA (Debortoli et al., 2016). ............................................ 14 Figure 3: Methodology Model ................................................................................................. 17 Figure 4: Sample user post before cleaning of code snippet from the text content. ................ 21 Figure 5: Sample user post after cleaning of code snippet from the text content. ................... 21 Figure 6: Title of sample user post .......................................................................................... 22 Figure 7: Body of sample user post ......................................................................................... 22 Figure 8: Combined title and body of sample user post text ................................................... 22 Figure 9: Sample text before pre-processing .......................................................................... 25 Figure 10: Sample text after partial pre-processing ................................................................. 25 Figure 11: Sample text before stemming and lemmatisation................................................... 26 Figure 12: Sample text after stemming and lemmatisation ....................................................
Load more

Recommended publications
  • An Empirical Study of Obsolete Answers on Stack Overflow
    1 An Empirical Study of Obsolete Answers on Stack Overflow Haoxiang Zhang, Shaowei Wang, Tse-Hsun (Peter) Chen, Ying Zou, Senior Member, IEEE, and Ahmed E. Hassan, Fellow, IEEE Abstract—Stack Overflow accumulates an enormous amount of software engineering knowledge. However, as time passes, certain knowledge in answers may become obsolete. Such obsolete answers, if not identified or documented clearly, may mislead answer seekers and cause unexpected problems (e.g., using an out-dated security protocol). In this paper, we investigate how the knowledge in answers becomes obsolete and identify the characteristics of such obsolete answers. We find that: 1) More than half of the obsolete answers (58.4%) were probably already obsolete when they were first posted. 2) When an obsolete answer is observed, only a small proportion (20.5%) of such answers are ever updated. 3) Answers to questions in certain tags (e.g., node.js, ajax, android, and objective-c) are more likely to become obsolete. Our findings suggest that Stack Overflow should develop mechanisms to encourage the whole community to maintain answers (to avoid obsolete answers) and answer seekers are encouraged to carefully go through all information (e.g., comments) in answer threads. Index Terms—Q&A Website, Stack Overflow, Obsolete Knowledge, Knowledge Sharing. F 1 INTRODUCTION ECHNICAL Q&A websites are becoming an important to handle such a situation2. Additionally, a survey of 453 T and popular platform for knowledge sharing and learn- Stack Overflow users reports that outdated code on Stack ing. They have revolutionized how users seek knowledge Overflow is one of the most important issues that users on the Internet.
    [Show full text]
  • Stack Overflow Question Retrieval System Vishi Cline Southern Methodist University, [email protected]
    SMU Data Science Review Volume 1 | Number 2 Article 13 2018 Stack Overflow Question Retrieval System Vishi Cline Southern Methodist University, [email protected] Abhishek Dharwadkar Southern Methodist University, [email protected] Rajni Goyal Southern Methodist University, [email protected] Daniel Engels Southern Methodist University, [email protected] Raghuram Srinivas Southern Methodist University, [email protected] See next page for additional authors Follow this and additional works at: https://scholar.smu.edu/datasciencereview Recommended Citation Cline, Vishi; Dharwadkar, Abhishek; Goyal, Rajni; Engels, Daniel; Srinivas, Raghuram; and Rafiqi, Sohail (2018) "Stack Overflow Question Retrieval System," SMU Data Science Review: Vol. 1 : No. 2 , Article 13. Available at: https://scholar.smu.edu/datasciencereview/vol1/iss2/13 This Article is brought to you for free and open access by SMU Scholar. It has been accepted for inclusion in SMU Data Science Review by an authorized administrator of SMU Scholar. For more information, please visit http://digitalrepository.smu.edu. Stack Overflow Question Retrieval System Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License Authors Vishi Cline, Abhishek Dharwadkar, Rajni Goyal, Daniel Engels, Raghuram Srinivas, and Sohail Rafiqi This article is available in SMU Data Science Review: https://scholar.smu.edu/datasciencereview/vol1/iss2/13 Cline et al.: Stack Overflow Question Retrieval System Stack Overflow Question Retrieval System Abhishek Dharwadkar1, Vishi Cline1, Rajni Goyal1 Daniel Engels2, Raghuram Srinivas2, Sohail Rafiqi2 1 Master of Science in Data Science, Southern Methodist University 6425 Boaz Lane, Dallas, TX 75205 {avdharwadkar, rajnig, vcline, rsrinivas}@mail.smu.edu {dwe, srafiqi}@lyle.smu.edu Abstract.
    [Show full text]
  • Glitch.Com What Is Glitch?
    glitch.com What is Glitch? “Everybody has good ideas of things we want to make on the Internet. But honestly, it’s gotten too hard. Gomix fixes that. If you’re not a professional developer, the barriers are way too high. There are pretty good tools if you just went to make a marketing website or a blog, but if your idea is an app or a bot or something more interactive, you’re out of luck. It ought to be as easy to make an app as it is to edit a blog post or to change a formula in a spreadsheet.” Anil Dash, December 6, 2016 https://medium.com/gomix/introducing-gomix-aec205c421cb#.fqutfey03 https://glitch.com/about/ Glitch handles the mess • While you work with Glitch, it will seamlessly upgrade your servers and cloud infrastructure in the background. • There’s no deployment or server provisioning because it all happens automatically. Backed by a real company Glitch • Glitch is made by Fog Creek, one of the most influential small tech companies in the world. • They made Trello, FogBugz and co-created Stack Overflow. Why did they make it In some ways, Glitch is a throwback to an older era of software or the internet, when there were simpler ways to get started making cool stuff. For people who were around at that time, they'll understand Glitch easily: It brings “View Source” back. The ability to just look at the code behind something, and tweak it, and make your own thing, was essential to making the Internet fun, and weird, and diverse, in its early days.
    [Show full text]
  • An Analysis of Python's Topics, Trends, and Technologies Through Mining Stack Overflow Discussions
    1 An Analysis of Python’s Topics, Trends, and Technologies Through Mining Stack Overflow Discussions Hamed Tahmooresi, Abbas Heydarnoori, and Alireza Aghamohammadi F Abstract—Python is a popular, widely used, and general-purpose pro- (LDA) [3] to categorize discussions in specific areas such gramming language. In spite of its ever-growing community, researchers as web programming [4], mobile application development [5], have not performed much analysis on Python’s topics, trends, and tech- security [6], and blockchain [7]. However, none of them has nologies which provides insights for developers about Python commu- focused on Python. In this study, we shed light on the nity trends and main issues. In this article, we examine the main topics Python’s main areas of discussion through mining 2 461 876 related to this language being discussed by developers on one of the most popular Q&A websites, Stack Overflow, as well as temporal trends posts, from August 2008 to January 2019, using LDA based through mining 2 461 876 posts. To be more useful for the software topic modeling . We also analyze the temporal trends of engineers, we study what Python provides as the alternative to popular the extracted topics to find out how the developers’ interest technologies offered by common programming languages like Java. is changing over the time. After explaining the topics and Our results indicate that discussions about Python standard features, trends of Python, we investigate the technologies offered web programming, and scientific programming 1 are the most popular by Python, which are not properly revealed by the topic areas in the Python community.
    [Show full text]
  • How to Communicate with Developers
    How to Communicate with Developers Employer branding, job listings, and emails that resonate with a tech audience Developers are one of the most in-demand groups of employees these days. And guess what, they noticed this as well: Countless approaches by potential employers, sometimes several messages a day through Linkedin, and a few desperate recruiters even cold calling. So those with technical talents are by no means oblivious to the talent shortage. The good news is that they don’t just use this bargaining power to simply negotiate the highest salaries and most outrageous benefits. Instead, developers are intrinsically motivated. They are looking for the right place to work. Your challenge in this noisy jobs market is to clearly communicate what defines your employer brand, what work needs doing, and, ultimately, who might be the right fit for the role. All of this is easier said than done. Because tech recruiting is a complex business, it is easy to not see the forest for the trees. This guide will help you decide where to start or what to fix next. In the first and more general part, we would like you to take a step back. Before we even think about how to package our job opening and approach a candidate with our offer, we look at what information and knowledge you should gather about your tech team and the company at large. Following that, we will take a practical look at how to write and talk about your company and the role, with a special focus on the job listings and a recruiting emails as a first introduction.
    [Show full text]
  • Mining Stack Overflow for Questions Asked by Web Developers
    Mining Stack Overflow for Questions Asked by Web Developers An Empirical Study by Kartik Bajaj B.Tech., VIT University, 2012 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in The Faculty of Graduate and Postdoctoral Studies (Electrical and Computer Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2014 © Kartik Bajaj 2014 Abstract Modern web applications consist of a significant amount of client-side code, written in JavaScript, HTML, and CSS. In this thesis, we present a study of common challenges and misconceptions among web developers, by mining related questions asked on Stack Overflow. We use unsupervised learning to categorize the mined questions and define a ranking algorithm to rank all the Stack Overflow questions based on their importance. We analyze the top 50 questions qualitatively. The results indicate that (1) the overall share of web development related discussions is increasing among developers, (2) browser related discussions are prevalent; however, this share is decreasing with time, (3) form validation and other DOM related discussions have been discussed consistently over time, (4) web related discussions are becoming more preva- lent in mobile development, and (5) developers face implementation issues with new HTML5 features such as Canvas. We examine the implications of the results on the development, research, and standardization communi- ties. Our results show that there is a consistent knowledge gap between the options available and options known to developers. Given the presence of knowledge gap among developers, we need better tools customized to assist developers in building web applications. ii Preface The thesis is an extension of empirical study of questions asked by web de- velopers on Stack Overflow conducted by myself in collaboration with Pro- fessor Karthik Pattabiraman and Professor Ali Mesbah.
    [Show full text]
  • Understanding and Predicting Software Developer Expertise in Stack Overflow and Github
    Understanding and Predicting Software Developer Expertise in Stack Overflow and GitHub by Sri Lakshmi Vadlamani A Thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfilment of the requirements for the degree of Master of Computer Science with Specialization in Data Science Carleton University Ottawa, Ontario ©2021 Sri Lakshmi Vadlamani The undersigned recommend to the Faculty of Graduate and Postdoctoral Affairs acceptance of the Thesis Understanding and Predicting Software Developer Expertise in Stack Overflow and GitHub Submitted by Sri Lakshmi Vadlamani in partial fulfilment of the requirements for the degree of Master of Computer Science Dr. Olga Baysal, Thesis Supervisor Dr. Alan Tsang, Internal Examiner Dr. Omair Shafiq, External Examiner Dr. Matthew Holden, Chair of Defence Dr. Michel Barbeau, Department Chair Carleton University 2021 ii Abstract Knowledge and experience are touted as both the necessary and sufficient condi- tions to make a person an expert. This thesis attempts to investigate this issue in the context of software development by studying and predicting software developer's expertise based on their activity and experience on GitHub and Stack Overflow plat- forms. The thesis studies how developers themselves define the notion of an \expert", as well as why or why not developers contribute to online collaborative platforms. An exploratory survey was conducted with 73 software developers and a mixed methods approach was applied to analyze the survey results. The results provided deeper insights into how an expert in the field could be defined. The study provides a better understanding of the underlying factors that drive developers to contribute to GitHub and Stack Overflow, and the challenges they face when participating on either platform.
    [Show full text]
  • Characteristics and Challenges of Low-Code Development: the Practitioners' Perspective
    Characteristics and Challenges of Low-Code Development: The Practitioners’ Perspective Yajing Luo1, Peng Liang1∗, Chong Wang1, Mojtaba Shahin2, Jing Zhan3 1School of Computer Science, Wuhan University, Wuhan, China 2Faculty of Information Technology, Monash University, Melbourne, Australia 3Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, United States {luoyajing, liangp, cwang}@whu.edu.cn, [email protected], [email protected] ABSTRACT ACM Reference Format: 1 1∗ 1 2 3 Background: In recent years, Low-code development (LCD) is Yajing Luo , Peng Liang , Chong Wang , Mojtaba Shahin , Jing Zhan . growing rapidly, and Gartner and Forrester have predicted that the 2021. Characteristics and Challenges of Low-Code Development: The Prac- titioners’ Perspective. In ACM / IEEE International Symposium on Empirical use of LCD is very promising. Giant companies, such as Microsoft, Software Engineering and Measurement (ESEM) (ESEM ’21), October 11–15, Mendix, and Outsystems have also launched their LCD platforms. 2021, Bari, Italy. ACM, New York, NY, USA, 11 pages. https://doi.org/10.1145/3475716.3475782 Aim: In this work, we explored two popular online developer communities, Stack Overflow (SO) and Reddit, to provide insights on the characteristics and challenges of LCD from a practitioners’ 1 INTRODUCTION perspective. Method: We used two LCD related terms to search With the growth of the Internet and the wave of digitalization, the relevant posts in SO and extracted 73 posts. Meanwhile, we there is a growing need for enterprises to make quick and resilient explored three LCD related subreddits from Reddit and collected responses to changing market requirements [15]. According to the 228 posts.
    [Show full text]
  • What Defines a Good Stack Overflow Answer Post, an Automated Post Rater Github Link: H Ttps://Github.Com/Yanpeitian/CS229
    What Defines a Good Stack Overflow Answer Post, An Automated Post Rater Github Link: h ttps://github.com/YanpeiTian/CS229 1. Abstract Major Q&A sites for professional and enthusiast programmers, like Stack Overflow, have gained phenomenal popularity during recent years. Programmers are relying on these sites to seek inspiration, find solutions and help others. However, many of the question owners do not select their most preferred answer. Therefore, it would be very inconvenient for other viewers to quickly identify useful information, especially for some unpopular questions with zero vote. This project attempts to address this issue by building an automated agent that can identify “good” answers. A feature extractor is first used to preprocess the data. Then, we apply logistic regression, SVM, and neural network to train our model. With our implementation, we can get ~70% prediction accuracy when measured against the accuracy metric1 . 2. Introduction As there are many desired qualities that define a good answer. We built our own feature extractor to gauge our data. The input to our feature extractor is the raw data obtained from the Stack Overflow online public archive. We used SQL to query related questions, answers, and user information from it. Then we preprocessed the data to obtain relevant features, such as comment count, post length, user reputation, user profile views, total user upvotes, total user down votes, the number of code words used in the description, the length of code blocks, hyperlinks (reference), and edit, etc. Other than the manually selected features mentioned above, we also considered the overall style and fluency of an answer.
    [Show full text]
  • Characterization and Prediction of Questions Without Accepted Answers on Stack Overflow
    Characterization and Prediction of Questions without Accepted Answers on Stack Overflow Mohamad Yazdaninia∗ David Lo Ashkan Sami Department of CSE and IT School of Information Systems Department of CSE and IT Shiraz University Singapore Management University Shiraz University Shiraz, Iran Singapore Shiraz, Iran [email protected] [email protected] [email protected] Abstract—A fast and effective approach to obtain information visitors use solved problems on Stack Overflow. To ask appro- regarding software development problems is to search them to priate questions, Stack Overflow helps users with hints about find similar solved problems or post questions on community on/off topics, the specificity of a question, and improvement question answering (CQA) websites. Solving coding problems in a short time is important, so these CQAs have a considerable tips [3]. In spite of all of these, our observations show the impact on the software development process. However, if devel- number of questions without accepted answers is increasing on opers do not get their expected answers, the websites will not Stack Overflow. Askers mark answers that solve their coding be useful, and software development time will increase. Stack problems as accepted answers [1]. We consider questions with Overflow is the most popular CQA concerning programming accepted answers as resolved questions; otherwise, we name problems. According to its rules, the only sign that shows a question poser has achieved the desired answer is the user’s them unresolved questions. Increasing the number of unre- acceptance. In this paper, we investigate unresolved questions, solved questions on Stack Overflow motivated us to inquire without accepted answers, on Stack Overflow.
    [Show full text]
  • Are Comments on Stack Overflow Well Organized for Easy Retrieval by Developers?
    — Are comments on Stack Overflow well organized for easy retrieval by developers? HAOXIANG ZHANG, Software Analysis and Intelligence Lab (SAIL), Queen’s University, Canada SHAOWEI WANG, Department of Computer Science, University of Manitoba, Canada TSE-HSUN (PETER) CHEN, Software PErformance, Analysis, and Reliability (SPEAR) Lab, Concordia University, Canada AHMED E. HASSAN, Software Analysis and Intelligence Lab (SAIL), Queen’s University, Canada Many Stack Overflow answers have associated informative comments that can strengthen them andassist developers. A prior study found that comments can provide additional information to point out issues in their associated answer, such as the obsolescence of an answer. By showing more informative comments (e.g., the ones with higher scores) and hiding less informative ones, developers can more effectively retrieve information from the comments that are associated with an answer. Currently, Stack Overflow prioritizes the display of comments and as a result, 4.4 million comments (possibly including informative comments) are hidden by default from developers. In this study, we investigate whether this mechanism effectively organizes informative comments. We find that: 1) The current comment organization mechanism does not work welldue to the large amount of tie-scored comments (e.g., 87% of the comments have 0-score). 2) In 97.3% of answers with hidden comments, at least one comment that is possibly informative is hidden while another comment with the same score is shown (i.e., unfairly hidden comments). The longest unfairly hidden comment is more likely to be informative than the shortest one. Our findings highlight that Stack Overflow should consider adjusting the comment organization mechanism to help developers effectively retrieve informative comments.
    [Show full text]
  • An Empirical Study of Obsolete Answers on Stack Overflow
    1 An Empirical Study of Obsolete Answers on Stack Overflow Haoxiang Zhang, Shaowei Wang, Tse-Hsun (Peter) Chen, Ying Zou, Senior Member, IEEE, and Ahmed E. Hassan, Fellow, IEEE Abstract—Stack Overflow accumulates an enormous amount of software engineering knowledge. However, as time passes, certain knowledge in answers may become obsolete. Such obsolete answers, if not identified or documented clearly, may mislead answer seekers and cause unexpected problems (e.g., using an out-dated security protocol). In this paper, we investigate how the knowledge in answers becomes obsolete and identify the characteristics of such obsolete answers. We find that: 1) More than half of the obsolete answers (58.4%) were probably already obsolete when they were first posted. 2) When an obsolete answer is observed, only a small proportion (20.5%) of such answers are ever updated. 3) Answers to questions in certain tags (e.g., node.js, ajax, android, and objective-c) are more likely to become obsolete. Our findings suggest that Stack Overflow should develop mechanisms to encourage the whole community to maintain answers (to avoid obsolete answers) and answer seekers are encouraged to carefully go through all information (e.g., comments) in answer threads. Index Terms—Q&A Website, Stack Overflow, Obsolete Knowledge, Knowledge Sharing. F 1 INTRODUCTION ECHNICAL Q&A websites are becoming an important to handle such a situation2. Additionally, a survey of 453 T and popular platform for knowledge sharing and learn- Stack Overflow users reports that outdated code on Stack ing. They have revolutionized how users seek knowledge Overflow is one of the most important issues that users on the Internet.
    [Show full text]