Mining version histories to guide software changes

437 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Survey of Machine Learning for Big Code and Naturalness (2017, background): “ “Big Code”isasynonymousneologism,createdbyDARPA’sMUSEprogram8 toborrowsomebranding shine from the well-marketed term “Big Data”. The MSR ﬁeld’s early successes date back to work by Zimmermann et al. [201], Williams and Hollingsworth [189], Gabel and Su [66] and Acharya etal.[1]onminingAPIprotocolsf”
Mining metrics to predict component failures (2006, background): “4 Mining Software Repositories In recent years, researchers have learned to exploit the vast amount of data that is contained in software repositories such as version and bug databases [16, 17, 19, 22].”
The promises and perils of mining GitHub (2009, methodology): “Researchers have also used this data to create recommender systems [3], [4], [5], study evolution patterns [6], [7], [8], predict bugs [9], [10], [11], and examine collaboration [12], [13], [14].”
Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction (2007, background): “For instance, Ying et al. or Zimmermann et al. developed approaches that guide programmers along related changes by telling them “programmers who changed these functions also changed . . . ” [45], [47].”