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Fault-detection, localization, and repair methods are vital to softwarequality; but it is difficult to evaluate their generality, applicability, andcurrent effectiveness. Large, diverse, realistic datasets ofdurably-reproducible faults and fixes are vital to good experimental evaluationof approaches to software quality, but they are difficult and expensive toassemble and keep current. Modern continuous-integration (CI) approaches, likeTravis-CI, which are widely used, fully configurable, and executed withincustom-built containers, promise a path toward much larger defect datasets. Ifwe can identify and archive failing and subsequent passing runs, the containerswill provide a substantial assurance of durable future reproducibility of buildand test. Several obstacles, however, must be overcome to make this a practicalreality. We describe BugSwarm, a toolset that navigates these obstacles toenable the creation of a scalable, diverse, realistic, continuously growing setof durably reproducible failing and passing versions of real-world, open-sourcesystems. The BugSwarm toolkit has already gathered 3,091 fail-pass pairs, inJava and Python, all packaged within fully reproducible containers.Furthermore, the toolkit can be run periodically to detect fail-passactivities, thus growing the dataset continually.

BugSwarm: Mining and Continuously Growing a Dataset of Reproducible Failures and Fixes