Predicting Fault Incidence Using Software Change History
IEEE Transactions on Software Engineering
DynaMine: finding common error patterns by mining software revision histories
Proceedings of the 10th European software engineering conference held jointly with 13th ACM SIGSOFT international symposium on Foundations of software engineering
A content-driven reputation system for the wikipedia
Proceedings of the 16th international conference on World Wide Web
Creating, destroying, and restoring value in wikipedia
Proceedings of the 2007 international ACM conference on Supporting group work
Computing trust from revision history
Proceedings of the 2006 International Conference on Privacy, Security and Trust: Bridge the Gap Between PST Technologies and Business Services
Sources of errors in distributed development projects: implications for collaborative tools
Proceedings of the 2010 ACM conference on Computer supported cooperative work
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As evidenced by SourceForge and GitHub, code repositories now integrate Web 2.0 functionality that enables global participation with minimal barriers-to-entry. To prevent detrimental contributions enabled by crowdsourcing, reputation is one proposed solution. Fortunately this is an issue that has been addressed in analogous version control systems such as the wiki for natural language content. The WikiTrust algorithm ("content-driven reputation"), while developed and evaluated in wiki environments operates under a possibly shared collaborative assumption: actions that "survive" subsequent edits are reflective of good authorship. In this paper we examine WikiTrust's ability to measure author quality in collaborative code development. We first define a mapping from repositories to wiki environments and use it to evaluate a production SVN repository with 92,000 updates. Analysis is particularly attentive to reputation loss events and attempts to establish ground truth using commit comments and bug tracking. A proof-of-concept evaluation suggests the technique is promising (about two-thirds of reputation loss is justified) with false positives identifying areas for future refinement. Equally as important, these false positives exemplify differences in content evolution and the cooperative process between wikis and code repositories.