Learning project management decisions : A case study with case-based reasoning versus data farming

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

  • Tim Menzies
  • Adam Brady
  • Jairus Hihn
  • Steven Williams
  • Oussama El-Rawas
  • Phillip Green
  • Barry Boehm

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number6600685
Pages (from-to)1698-1713
Journal / PublicationIEEE Transactions on Software Engineering
Volume39
Issue number12
Online published13 Sep 2013
Publication statusPublished - Dec 2013

Abstract

Background: Given information on just a few prior projects, how do we learn the best and fewest changes for current projects? Aim: To conduct a case study comparing two ways to recommend project changes. 1) Data farmers use Monte Carlo sampling to survey and summarize the space of possible outcomes. 2) Case-based reasoners (CBR) explore the neighborhood around test instances. Method: We applied a state-of-the data farmer (SEESAW) and a CBR tool (({\cal W}2)) to software project data. Results: CBR with ({\cal W}2) was more effective than SEESAW's data farming for learning best and recommended project changes, effectively reducing runtime, effort, and defects. Further, CBR with ({\cal W}2) was comparably easier to build, maintain, and apply in novel domains, especially on noisy data sets. Conclusion: Use CBR tools like ({\cal W}2) when data are scarce or noisy or when project data cannot be expressed in the required form of a data farmer. Future Work: This study applied our own CBR tool to several small data sets. Future work could apply other CBR tools and data farmers to other data (perhaps to explore other goals such as, say, minimizing maintenance effort). © 1976-2012 IEEE.

Research Area(s)

  • case-based reasoning, COCOMO, data farming, Search-based software engineering

Citation Format(s)

Learning project management decisions : A case study with case-based reasoning versus data farming. / Menzies, Tim; Brady, Adam; Keung, Jacky; Hihn, Jairus; Williams, Steven; El-Rawas, Oussama; Green, Phillip; Boehm, Barry.

In: IEEE Transactions on Software Engineering, Vol. 39, No. 12, 6600685, 12.2013, p. 1698-1713.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review