Diggit: Automated code review via software repository mining

We present Diggit, a tool to automatically generate code review comments, offering design guidance on prospective changes, based on insights gained from mining historical changes in source code repositories. We describe how the tool was built and tuned for use in practice as we integrated Diggit into the working processes of an industrial development team. We focus on the developer experience, the constraints that had to be met in adapting academic research to produce a tool that was useful to developers, and the effectiveness of the results in practice.

[1]  Cristiano Calcagno,et al.  Infer: An Automatic Program Verifier for Memory Safety of C Programs , 2011, NASA Formal Methods.

[2]  David Lorge Parnas,et al.  Software aging , 1994, Proceedings of 16th International Conference on Software Engineering.

[3]  Andreas Zeller,et al.  Mining Version Histories to Guide Software Changes , 2004 .

[4]  Christian Borgelt,et al.  An implementation of the FP-growth algorithm , 2005 .

[5]  Rakesh Agarwal,et al.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

[6]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

[7]  Susan Elliott Sim,et al.  The ramp-up problem in software projects: a case study of how software immigrants naturalize , 1998, Proceedings of the 20th International Conference on Software Engineering.

[8]  Shane McIntosh,et al.  The impact of code review coverage and code review participation on software quality: a case study of the qt, VTK, and ITK projects , 2014, MSR 2014.

[9]  Jerry Fitzpatrick,et al.  Applying the ABC metric to C, C++, and Java , 2000 .

[10]  Martin Fowler,et al.  Refactoring - Improving the Design of Existing Code , 1999, Addison Wesley object technology series.

[11]  Lucy M. Berlin,et al.  Beyond Program Understanding: A Look at Programming Expertise in Industry , 2001 .

[12]  Mauricio A. Saca.  Refactoring improving the design of existing code , 2017, 2017 IEEE 37th Central America and Panama Convention (CONCAPAN XXXVII).

[13]  Dave W. Binkley,et al.  Generalizing the Analysis of Evolutionary Coupling for Software Change Impact Analysis , 2016, 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER).

[14]  Kent L. Beck,et al.  Extreme programming explained - embrace change , 1990 .

[15]  Michael W. Godfrey,et al.  Reading Beside the Lines: Indentation as a Proxy for Complexity Metric , 2008, 2008 16th IEEE International Conference on Program Comprehension.