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Modern, complex software systems are being continuously extended andadjusted. The developers responsible for this may come from different teams ororganizations, and may be distributed over the world. This may make itdifficult to keep track of what other developers are doing, which may result inmultiple developers concurrently editing the same code areas. This, in turn,may lead to hard-to-merge changes or even merge conflicts, logical bugs thatare difficult to detect, duplication of work, and wasted developerproductivity. To address this, we explore the extent of this problem in thepull request based software development model. We study half a year of changesmade to six large repositories in Microsoft in which at least 1,000 pullrequests are created each month. We find that files concurrently edited indifferent pull requests are more likely to introduce bugs. Motivated by thesefindings, we design, implement, and deploy a service named ConE (ConcurrentEdit Detector) that proactively detects pull requests containing concurrentedits, to help mitigate the problems caused by them. ConE has been designed toscale, and to minimize false alarms while still flagging relevant concurrentlyedited files. Key concepts of ConE include the detection of the Extent ofOverlap between pull requests, and the identification of Rarely ConcurrentlyEdited Files. To evaluate ConE, we report on its operational deployment on 234repositories inside Microsoft. ConE assessed 26,000 pull requests and made 775recommendations about conflicting changes, which were rated as useful in over70% (554) of the cases. From interviews with 48 users we learned that theybelieved ConE would save time in conflict resolution and avoiding duplicatework, and that over 90% intend to keep using the service on a daily basis.

ConE: A Concurrent Edit Detection Tool for Large Scale Software Development