Mining and Fusing Productivity Metrics with Code Quality Information at Scale
Productivity in software development is a complex, multi-faceted concept expressed as a combination of effectiveness and efficiency. From a quantitative lens, productivity is often interpreted from a collection of activities and metrics such as the number of commits, lines of code added and removed, and the number of issues closed. Software development team managers often seek to track developers’ activity and productivity for short-term planning and medium-term team performance measurement. Existing tools and platforms analyze and visualize individual aspects of developers’ activity, productivity, or quality. However, a tool that fuses multiple information streams representing productivity and quality aspects is missing. The proposed tool QConnect fills the gap by mining, analyzing, and fusing information from software development-relevant streams. QConnect, on the one hand, mines the repository and issue tracking metadata from GitHub and Jira issue tracking system; on the other hand, it gathers information related to code quality using external tools Designite and RefactoringMiner. By tying-in productivity measures with code quality information, stakeholders can assess not only how fast but also how well the project is progressing. Website: https://qconnect.dev Demo video: https://youtu.be/MFqYVQnWxgQ
Wed 4 OctDisplayed time zone: Bogota, Lima, Quito, Rio Branco change
10:30 - 12:00 | Software QualityJournal First Track / Tool Demo Track / New Ideas and Emerging Results Track / Research Track at Session 2 Room - RGD 04 Chair(s): Valentina Lenarduzzi University of Oulu, César França Universidade Federal Rural de Pernambuco | ||
10:30 16mTalk | Featherweight Assisted Vulnerability Discovery Journal First Track David Binkley Loyola University Maryland, Leon Moonen Simula Research Laboratory and BI Norwegian Business School, Sibren Isaacman Loyola University Maryland | ||
10:46 11mTalk | DebtViz: A Tool for Identifying, Measuring, Visualizing, and Monitoring Self-Admitted Technical Debt Tool Demo Track Yikun Li University of Groningen, Mohamed Soliman , Paris Avgeriou University of Groningen, The Netherlands, Maarten van Ittersum | ||
10:57 11mTalk | Mining and Fusing Productivity Metrics with Code Quality Information at Scale Tool Demo Track Harsh Mukeshkumar Shah Dalhousie University, Qurram Zaheer Syed , Bharatwaaj Shankaranarayanan , Indranil Palit Dalhousie University, Arshdeep Singh , Kavya Raval , Kishan Savaliya , Tushar Sharma Dalhousie University Pre-print | ||
11:08 16mTalk | An Investigation of Confusing Code Patterns in JavaScript Journal First Track Adriano Torres Computer Science Department, University of Brasília, Caio Oliveira Computer Science Department, University of Brasília, Marcio Okimoto Computer Science Department, University of Brasília, Diego Marcilio USI Università della Svizzera italiana, Pedro Queiroga Informatics Center, Federal University of Pernambuco, Fernando Castor Utrecht University & Federal University of Pernambuco, Rodrigo Bonifácio Computer Science Department - University of Brasília, Edna Dias Canedo University of Brasilia (UnB), Márcio Ribeiro Federal University of Alagoas, Brazil, Eduardo Monteiro Statistics Department, University of Brasília | ||
11:24 11mTalk | StaticTracker: A Diff Tool for Static Code Warnings Tool Demo Track | ||
11:35 11mTalk | Capturing Contextual Relationships of Buggy Classes for Detecting Quality-Related Bugs New Ideas and Emerging Results Track | ||
11:46 14mLive Q&A | 1:1 Q&A Research Track | ||