Learning Project Management Decisions: A Case Study with Case-Based Reasoning versus Data Farming.
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 (W2) to software project data. Results: CBR with W2 was more effective than SEESAW's data farming for learning best and recommended project changes, effectively reducing runtime, effort, and defects. Further, CBR with W2 was comparably easier to build, maintain, and apply in novel domains, especially on noisy data sets. Conclusion: Use CBR tools like W2 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). Index Terms-Search-based software engineering, case-based reasoning, data farming, COCOMO Ç 1 I INTRODUCTION N the age of Big Data and cloud computing, it is tempting to tackle problems using: a data-intensive Google-style collection of gigabytes of data, or, when that data are missing .... . a CPU-intensive data farming analysis, i.e., Monte Carlo sampling [1] to survey and summarize the space of possible outcomes (for details on data farming, see Section 2). For example, consider a software project manager trying to . . reduce project defects in the delivered software and . reduce project development effort. How can a manager find and assess different ways to address these goals? It may not be possible to answer this question via data-intensive methods. Such data are inherently . T. Menzies, A. Brady, O. El-Rawas, and P. Green are with the Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506. E-mail: tim@menzies.us, adam.m.brady@gmail.com, orawas@gmail.com, deathcheese@yahoo.com