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Mining valence, arousal, and dominance: possibilities for detecting burnout and productivity?

, , , , and . MSR, page 247-258. ACM, (2016)

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Studying software evolution using topic models., , , and . Sci. Comput. Program., (2014)A Large-Scale Empirical Study of the Relationship between Build Technology and Build Maintenance., , , , and . Empirical Software Engineering, 20 (6): 1587-1633 (2015)On the effect of program exploration on maintenance tasks., , , , and . WCRE, page 391-400. IEEE Computer Society, (2013)Workshop on Mining Unstructured Data (MUD) because "Mining Unstructured Data is Like Fishing in Muddy Waters"!, and . WCRE, page 277-278. IEEE Computer Society, (2010)Monitoring sentiment in open source mailing lists: exploratory study on the apache ecosystem., , and . CASCON, page 34-44. IBM / ACM, (2014)Replicating and Re-Evaluating the Theory of Relative Defect-Proneness., , , and . IEEE Trans. Software Eng., 41 (2): 176-197 (2015)Assisting developers of big data analytics applications when deploying on hadoop clouds., , , , , and . ICSE, page 402-411. IEEE Computer Society, (2013)Make it simple: an empirical analysis of GNU make feature use in open source projects., , , and . ICPC, page 207-217. IEEE Computer Society, (2015)An empirical study of integration activities in distributions of open source software., , , and . Empirical Software Engineering, 21 (3): 960-1001 (2016)Does Geographical Distance Effect Distributed Development Teams: How Aggregation Bias in Software Artifacts Causes Contradictory Findings., , and . ISSRE, page 412-423. IEEE Computer Society, (2016)