ERF: An Empirical Recommender Framework for Ascertaining Appropriate Learning Materials from Stack Overflow Discussions
Computer programmers require various instructive information during coding and development. Such information is dispersed in different sources like language documentation, wikis, and forums. As an information exchange platform, programmers broadly utilize Stack Overflow, a Web-based Question Answeri...
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MDPI AG
2020-07-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/9/3/57 |
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doaj-a0d10e3181924f858ee8376c8c641e8c2020-11-25T03:30:08ZengMDPI AGComputers2073-431X2020-07-019575710.3390/computers9030057ERF: An Empirical Recommender Framework for Ascertaining Appropriate Learning Materials from Stack Overflow DiscussionsAshesh Iqbal0Sumi Khatun1Mohammad Shamsul Arefin2M. Ali Akber Dewan3Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, BangladeshDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka 1216, BangladeshDepartment of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, BangladeshSchool of Computing and Information Systems, Faculty of Science and Technology, Athabasca University, Edmonton, AB T5J 3S8, CanadaComputer programmers require various instructive information during coding and development. Such information is dispersed in different sources like language documentation, wikis, and forums. As an information exchange platform, programmers broadly utilize Stack Overflow, a Web-based Question Answering site. In this paper, we propose a recommender system which uses a supervised machine learning approach to investigate Stack Overflow posts to present instructive information for the programmers. This might be helpful for the programmers to solve programming problems that they confront with in their daily life. We analyzed posts related to two most popular programming languages—Python and PHP. We performed a few trials and found that the supervised approach could effectively manifold valuable information from our corpus. We validated the performance of our system from human perception which showed an accuracy of 71%. We also presented an interactive interface for the users that satisfied the users’ query with the matching sentences with most instructive information.https://www.mdpi.com/2073-431X/9/3/57text classificationsupervised learningcrowd knowledgerecommender system |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Ashesh Iqbal Sumi Khatun Mohammad Shamsul Arefin M. Ali Akber Dewan |
| spellingShingle |
Ashesh Iqbal Sumi Khatun Mohammad Shamsul Arefin M. Ali Akber Dewan ERF: An Empirical Recommender Framework for Ascertaining Appropriate Learning Materials from Stack Overflow Discussions Computers text classification supervised learning crowd knowledge recommender system |
| author_facet |
Ashesh Iqbal Sumi Khatun Mohammad Shamsul Arefin M. Ali Akber Dewan |
| author_sort |
Ashesh Iqbal |
| title |
ERF: An Empirical Recommender Framework for Ascertaining Appropriate Learning Materials from Stack Overflow Discussions |
| title_short |
ERF: An Empirical Recommender Framework for Ascertaining Appropriate Learning Materials from Stack Overflow Discussions |
| title_full |
ERF: An Empirical Recommender Framework for Ascertaining Appropriate Learning Materials from Stack Overflow Discussions |
| title_fullStr |
ERF: An Empirical Recommender Framework for Ascertaining Appropriate Learning Materials from Stack Overflow Discussions |
| title_full_unstemmed |
ERF: An Empirical Recommender Framework for Ascertaining Appropriate Learning Materials from Stack Overflow Discussions |
| title_sort |
erf: an empirical recommender framework for ascertaining appropriate learning materials from stack overflow discussions |
| publisher |
MDPI AG |
| series |
Computers |
| issn |
2073-431X |
| publishDate |
2020-07-01 |
| description |
Computer programmers require various instructive information during coding and development. Such information is dispersed in different sources like language documentation, wikis, and forums. As an information exchange platform, programmers broadly utilize Stack Overflow, a Web-based Question Answering site. In this paper, we propose a recommender system which uses a supervised machine learning approach to investigate Stack Overflow posts to present instructive information for the programmers. This might be helpful for the programmers to solve programming problems that they confront with in their daily life. We analyzed posts related to two most popular programming languages—Python and PHP. We performed a few trials and found that the supervised approach could effectively manifold valuable information from our corpus. We validated the performance of our system from human perception which showed an accuracy of 71%. We also presented an interactive interface for the users that satisfied the users’ query with the matching sentences with most instructive information. |
| topic |
text classification supervised learning crowd knowledge recommender system |
| url |
https://www.mdpi.com/2073-431X/9/3/57 |
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