Crowdsourcing suggestions to programming problems for dynamic web development languages

Mujumdar, Dhawal; Kallenbach, Manuel; Liu, Brandon; Hartmann, Björn

doi:10.1145/1979742.1979802

Cited by 20 publications

(7 citation statements)

References 8 publications

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“…Despite numerous attempts for automation [5,15,19,35,68], software debugging is still largely a manual process which costs a significant amount of development time and efforts [4,37,61]. One of the Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Improving IR-based bug localization with context-aware query reformulation

Rahman

Roy

2018

Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Recent findings suggest that Information Retrieval (IR)-based bug localization techniques do not perform well if the bug report lacks rich structured information (e.g., relevant program entity names). Conversely, excessive structured information (e.g., stack traces) in the bug report might not always help the automated localization either. In this paper, we propose a novel technique-BLIZZARD-that automatically localizes buggy entities from project source using appropriate query reformulation and effective information retrieval. In particular, our technique determines whether there are excessive program entities or not in a bug report (query), and then applies appropriate reformulations to the query for bug localization. Experiments using 5,139 bug reports show that our technique can localize the buggy source documents with 7%-56% higher Hit@10, 6%-62% higher MAP@10 and 6%-62% higher MRR@10 than the baseline technique. Comparison with the state-of-the-art techniques and their variants report that our technique can improve 19% in MAP@10 and 20% in MRR@10 over the state-of-the-art, and can improve 59% of the noisy queries and 39% of the poor queries. CCS CONCEPTS• Software and its engineering → Software verification and validation; Software testing and debugging;

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Section: Introductionmentioning

confidence: 99%

Improving IR-based bug localization with context-aware query reformulation

Rahman

Roy

2018

Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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“…However, HelpMeOut only supports static, compiled programming languages such as Java. To further support dynamic, interpreted web programming languages, another tool named Crowd::Debug [199] was proposed.…”

Section: Crowdsourcing For Software Codingmentioning

confidence: 99%

A survey of the use of crowdsourcing in software engineering

Mao

Capra

Harman

et al. 2017

Journal of Systems and Software

302

231

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Crowdsourcing can be used to support software engineering activities and research into these activities. In this paper we provide a comprehensive survey of the use of crowdsourcing to support software engineering activities (Crowdsourced Software Engineering), seeking to cover all literature on this topic. We describe the software engineering domains, tasks and applications for crowdsourcing and the platforms and stakeholders involved in realising Crowdsourced Software Engineering solutions. We also expose trends, issues and opportunities for Crowdsourced Software Engineering.Please cite as: Ke Mao, Licia Capra, Mark Harman and Yue Jia. A Survey of the Use of Crowdsourcing in SoftwareEngineering.

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“…This latter social recommender system was also applied to Arduino. In addition, Crowd::Debug [7] was presented as a similar solution for Ruby, a test-driven development language. In terms of notifying students how they are progressing throughout a semester, Purdue University's Course Signals [8] sends a personalised mail and posts a traffic signal as an indicator of their performance while Dublin City University's PredictED [9] project notifies students how they are doing and where they are within their own class.…”

Section: Related Workmentioning

confidence: 99%

Personalizing Computer Science Education by Leveraging Multimodal Learning Analytics

Azcona

Hsiao

Smeaton

2018

2018 IEEE Frontiers in Education Conference (FIE)

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This Research Full Paper implements a framework that harness sources of programming learning analytics on three computer programming courses a Higher Education Institution. The platform, called PredictCS, automatically detects lowerperforming or "at-risk" students in programming courses and automatically and adaptively sends them feedback. This system has been progressively adopted at the classroom level to improve personalized learning. A visual analytics dashboard is developed and accessible to Faculty. This contains information about the models deployed and insights extracted from student's data. By leveraging historical student data we built predictive models using student characteristics, prior academic history, logged interactions between students and online resources, and students' progress in programming laboratory work. Predictions were generated every week during the semester's classes. In addition, during the second half of the semester, students who opted-in received pseudo real-time personalised feedback. Notifications were personalised based on students' predicted performance on the course and included a programming suggestion from a topstudent in the class if any programs submitted had failed to meet the specified criteria. As a result, this helped students who corrected their programs to learn more and reduced the gap between lower and higher-performing students.

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Crowdsourcing suggestions to programming problems for dynamic web development languages

Cited by 20 publications

References 8 publications

Improving IR-based bug localization with context-aware query reformulation

Improving IR-based bug localization with context-aware query reformulation

A survey of the use of crowdsourcing in software engineering

Personalizing Computer Science Education by Leveraging Multimodal Learning Analytics

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