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Stack Overflow slides Data Analytics
The document analyzes trends and popular topics in programming languages using data from Stack Overflow, one of the largest developer platforms. It discusses methodologies for predicting response times to questions using machine learning and highlights significant temporal trends and major discussion topics in programming languages. Future work includes enhancing the analysis with additional attributes and contributions to the community.
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Introduction to a study using StackOverflow data to analyze programming languages and trends.
Utilizes StackOverflow as a significant dataset to examine the trends and effectiveness of programming languages.
Discusses analysis objectives and references related studies on programming languages and response time.
Presents basic analysis trends on programming languages, detailing engagement and distribution of queries.
Explores detailed analyses of topics discussed on StackOverflow and how they relate to response time.
Describes the attributes and tools used for predicting response time for StackOverflow questions.
Presents evaluation strategies, results of classifiers, and various analyses of response times.
Summarizes the findings of the study and outlines future research directions and enhancements.
Opens the floor for questions about the presented study and findings.
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Stack Overflow slides Data Analytics
- 1. What StackOverflow TellsUs About Programming Languages Rahul Thankachan Nada Aldarrab Prathmesh Gat University of Southern California 1
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- 3. Agenda 1. Introduction 2. ProblemStatement 3. Temporal Based Trend Analysis 4. Topic Analysis 5. Predicting Time To Answer 6. Summary and Q&A 3
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- 5. Introduction • Dataset Used: Stackoverflow - Internet Archive. • Why? It is one of the largest developer focused open collaborative platform currently. • Through our study we intend to answers some interesting questions • Study the rise and fall of popular programming languages • Can be used to predict future enhancements • Study effectiveness of Stack Overflow model 5
- 6. Problem Definition • BasicAnalysis: What are the most popular programming languages? • What are the trends in programming languages? • What are the most popular topics discussed in a programming language? • Can we accurately predict the time it takes until a questioner gets an answer? 6
- 7. Related Work Miltiadis Allamanisand Charles Sutton. 2013. Why, When and What: Analyzing Stack Overflow Questions by Topic, Type & Code In 10th Working Conference on Mining Software Repositories. Mining Challenge. IEEE, pages 53- 56. • Topic modeling analysis • Used Latent Dirichlet Allocation (LDA) • Modeled Java Topics of Questions • Can evaluate the orthogonality of different languages • Stack Overflow questions are about the code and are not application domain specific 7
- 8. Related Work V. Bhat,A. Gokhale, R. Jadhav, J. Pudipeddi, and L. Akoglu. Min (e) d your tags: Analysis of question response time in stackoverflow. In Proceedings of ASONAM 2014, pages 328–335. IEEE, 2014. • Two linear classifiers: logistic regression and SVM with linear kernel • Two non- linear classifiers: decision tree (DT) and SVM with radial basis function kernel 8
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- 11. StackOverFlow Activity 11 Approx. 45%of all programming languages in world are discussed on StackOverflow!
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- 22. Approach Following attributes selectedfor study: 1. Tag (Only top 10 programming languages) 2. Creation Month 3. Body Length 4. Tag Length 5. Introduced new Nominal class - Time_Answer 6. (less6, bet6and20, 20andmore) 22
- 23. Approach Tools Weka - Wekais a collection of machine learning algorithms for data mining tasks. Data Preprocessing: Challenging! 23
- 24. Approach(Data Pre -processing) 1.Parse all the answers and link first answer’s creation time to creation time of question. We called this field delta-answer. 2. Remove all the Questions which had delta answer negative or zero 3. We developed a Python script which develops .arff file On the fly (Wish to contribute this file) 24
- 25. Evaluation • Subset Size:4490947 - Subset - 449000 • Classify response time into 3 types: less than 6 minutes, between 6 and 20 minutes, 20 minutes and more. • 10-fold cross-validation • Results are obtained using different feature combinations and different classifiers 25
- 26. Evaluation Results of classifierJ48 (all Attributes) 26
- 27. Evaluation 27 Results of classifier(body_length/ tag_length)
- 28. Summary • We weresuccessfully able to find interesting temporal trends for major programming languages • Using tag based topic analysis we were able to find major discussion topics and to some extent the difficult topics in a programming language • Using machine learning techniques we were successfully able to predict - time to answer with good accuracy 28
- 29. Future Scope ofWork • Contribute the .arff on the fly generator script. • Adding Parts of speech as an attribute • Showcasing the results on a website 29
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