Learning Effective Changes for Software Projects

32nd Intl. Conference on Automated Software Engineering Doctoral Symposium

Recommended citation: Krishna, R. “Learning effective changes for software projects”. 32nd Intl. Conference on Automated Software Engineering Doctoral Symposium, October 2017. Available: http://dl.acm.org/citation.cfm?id=3155562.3155695; https://arxiv.org/abs/1708.04589


The primary motivation of much of software analytics is decision making. How to make these decisions? Should one make decisions based on lessons that arise from within a particular project? Or should one generate these decisions from across multiple projects? This work is an attempt to answer these questions. Our work was motivated by a realization that much of the current generation software analytics tools focus primarily on prediction. Indeed prediction is a useful task, but it is usually followed by “planning” about what actions need to be taken. This research seeks to address the planning task by seeking methods that support actionable analytics that offer clear guidance on what to do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set of actionable plans within and across projects. Each of these plans, if followed will improve the quality of the software project.


  author    = {Rahul Krishna},
  title     = {Don't Tell Me What Is, Tell Me What Ought To Be! Learning Effective
               Changes for Software Projects},
  journal   = {CoRR},
  volume    = {abs/1708.04589},
  year      = {2017},
  url       = {http://arxiv.org/abs/1708.04589},
  archivePrefix = {arXiv},
  eprint    = {1708.04589},
  timestamp = {Tue, 05 Sep 2017 10:03:46 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/abs-1708-04589},
  bibsource = {dblp computer science bibliography, http://dblp.org}