Friday, May 20, 2016

ICSE 2016 presentations

Type: Presentation

Venue: ICSE 2016 (Doctoral Symposium and CSI-SE workshop)


I presented the following papers in ICSE 2016 and collocated events:


Left: Leonardo Mariani (University of Milan Bicocca)
Right: James Clause (University of Delaware)


Friday, February 26, 2016

Measuring User Influence in Github: The Million Follower Fallacy

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Type: Publication (accepted)

Venue: 3rd International Workshop on CrowdSourcing In Software Engineering; CSI-SE 2016
May 16, 2016, Austin, TX, USA

Authors: Ali Sajedi Badashian, Eleni Stroulia
Department of Computing Science, University of Alberta, Canada

Abstract
Influence in social networks has been extensively studied for collaborative-filtering recommendations and marketing purposes. We are interested in the notion of influence in Software Social Networks (SSNs); more specifically, we want to answer the following questions: 1) What does “influence” mean in SSNs? Given the variety of types of interactions supported in these networks and the abundance of centrality-type metrics, what is the nature of the influence captured by these matrics? 2) Are there silos of influence in these platforms or does influence span across thematic boundaries?

To investigate these two questions, we first conducted an in-depth comparison of three influence metrics, number of followers, number of forked projects, and number of project watchers in GitHub1 (the largest code-sharing and versioncontrol system). Next, we examined how the influence of the top software engineering people in GitHub is spread over different programming languages.

Our results indicate (a) that the three influence metrics capture two major characteristics: popularity and content value (code reusability) and (b) that the influence of influentials is spread over more than one programming language, but there is no specific trend toward any two programming languages.

Keywords:
Software engineering, influence, Software repositories, programming languages, software social network analysis, crowdsourcing

Saturday, January 23, 2016

Realistic Bug Triaging

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Type: Publication (accepted)

Venue: The 38th International Conference on Software Engineering
May 14-22, 2016, Austin, TX, USA

Authors: Ali Sajedi Badashian
Department of Computing Science, University of Alberta, Canada

Abstract
Bug triaging, i.e., assigning a bug report to the developer “best” able to address it, involves identifying a list of developers qualified to understand and address the bug report and ranking them according to their expertise. Most research in this area addresses this task by matching the description of the bug report and the developers’ prior development and bug-fixing activities.
This thesis puts forward a more realistic formulation of the bug-triaging task. First, we develop a novel model of the developers’ expertise, taking into account relevant evidence from their code-development conributions, as well as their contributions to relevant Question-and-Answer (Q&A) platforms. Second, we adopt an economics perspective to the task, and we propose to generalize bug-triaging from“assigning one bug to the best developer”to“cost-effectively assigning multiple peding bugs to a set of qualified and available developers.” In this paper, we report on our early results on the value of broadening the notion of developer’s expertise to take into account evidence from Q&Aplatforms.

Keywords:
Bug triaging, bug assignment, expertise retrieval, crowdsoucring, GitHub, Stack Overflow

Friday, December 18, 2015

Crowdsourced Bug Triaging: Leveraging Q&A Platforms for Bug Assignment

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Type: Publication (accepted)

Venue: 19th International Conference on Fundamental Approaches to Software Engineering (FASE)
April 2-8, 2016, Eindhoven, The Netherlands

Authors: Ali Sajedi Badashian, Abram Hindle, Eleni Stroulia
Department of Computing Science, University of Alberta, Canada

Abstract
Bug triaging, i.e., assigning a bug report to the “best” person to address it, involves identifying a list of developers that are qualified to understand and address the bug report, and then ranking them according to their expertise. Most research in this area examines the description of the bug report and the developers’ prior development and bug-fixing activities. In this paper, we propose a novel method that exploits a new source of evidence for the developers’ expertise, namely their contributions in Stack Overflow, the popular software Question and Answer (Q&A) platform. The key intuition of our method is that the questions a developer asks and answers in Stack Overflow, or more generally in software Q&A platforms, can potentially be an excellent indicator of his/her expertise. Motivated by this idea, our method uses the bug-report description as a guide for selecting relevant Stack Overflow contributions on the basis of which to identify developers with the necessary expertise to close the bug under examination. We evaluated this method in the context of the 20 largest GitHub projects, considering 7144 bug reports. Our results demonstrate that our method exhibits superior accuracy to other state-of-the-art methods.

Keywords:
Bug triaging, bug assignment, crowdsourcing, GitHub, Stack Overflow

Thursday, November 5, 2015

Featured presentation; "Crowdsourced Bug Triaging"

Type: Presentation

Venue: Consortium for Software Engineering Research (CSER) Fall 2015, Markham, ON, Canada

I presented the recently published ICSME 2015 paper again in the CSER fall meeting (Sunday November 1st 2015) at Markham, Ontario.

Github's Big Data Adaptor: An Eclipse Plugin

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Type: Publication

Venue: Conference of the Centre for Advanced Studies on Collaborative Research (CASCON) 2015, November 2-4, Markham, ON, Canada

Authors: Ali Sajedi Badashian*, Vraj Shah**, Eleni Stroulia*
* Department of Computing Science, University of Alberta, Canada
** Indian Institute of Technology

Abstract
The data of GitHub, the most popular code-sharing platform, fits the characteristics of “big data” (Volume, Variety and Velocity). To facilitate studies on this huge GitHub data volume, the GHTorrent web-site publishes a MYSQL dump of (some) GitHub data quarterly. Unfortunately, developers using these published data dumps face challenges with respect to the time required to parse and ingest the data, the space required to store it, and the latency of their queries. To help address these challenges, we developed a data adaptor as an Eclipse plugin, which efficiently handles this dump. The plugin offers an interactive interface through which users can explore and select any field in any table. After extracting the data selected by the user, the parser exports it in easyto-use spreadsheets. We hope that using this plugin will facilitate further studies on the GitHub data as a whole.

Keywords:
GitHub, Mining software repositories, Eclipse Plugin, Software tools, Big data

Friday, August 21, 2015

Crowdsourced Bug Triaging

[Download Pdf]

Type: Publication

Venue: 31st International Conference on Software Maintenance and Evolution (ICSME2015)
Sep 29 - Oct 1, 2015, Bremen, Germany

Authors: Ali Sajedi Badashian, Abram Hindle, Eleni Stroulia
Department of Computing Science, University of Alberta, Canada

Abstract
Bug triaging and assignment is a time-consuming task in big projects. Most research in this area examines the developers’ prior development and bug-fixing activities in order to recognize their areas of expertise and assign to them relevant bug fixes. We propose a novel method that exploits a new source of evidence for the developers’ expertise, namely their contributions to Q&A platforms such as Stack Overflow. We evaluated this method in the context of the 20 largest GitHub projects, considering 7144 bug reports. Our results demonstrate that our method exhibits superior accuracy to other state-of-theart methods, and that future bug-assignment algorithms should consider exploring other sources of expertise, beyond the project’s version-control system and bug tracker.

Keywords:
Bug Triaging, Bug Assignment, Software Social Networks, Expertise Seeking, Github, Stack Overflow