Constraint-Based Recommendation for Software Project Effort Estimation

Identifying the most appropriate effort estimation methods is an important aspect for software project management. Within the scope of an software industry cluster project an expert system recommending estimation methods that best match the software development project’s characteristics and context has been developed. The knowledgebased recommender exploits an explicit knowledge base in order to infer matching items based on the software project’s context. The contribution of this article lies in presenting a constraint-based reasoning mechanism for computing recommendable items from a large set of choices and in its application to the domain of software project management. It discusses a recommendation model for effort estimation methods and presents specific extensions like explanation and repair mechanisms that proved exceptionally useful in this application domain. The application was conceptualized and developed in an iterative process and results from two rounds of evaluation are reported.

[1]  Francesco Ricci,et al.  Supporting User Query Relaxation in a Recommender System , 2004, EC-Web.

[2]  Markus Zanker,et al.  Development of a Collaborative and Constraint-Based Web Configuration System for Personalized Bundling of Products and Services , 2007, WISE.

[3]  Dietmar Jannach.  Finding Preferred Query Relaxations in Content-based Recommenders , 2007 .

[4]  Harald C. Gall,et al.  Recommending method invocation context changes , 2008, RSSE '08.

[5]  Lior Rokach,et al.  Recommender Systems Handbook , 2010 .

[6]  Walid Maalej,et al.  Potentials and challenges of recommendation systems for software development , 2008, RSSE '08.

[7]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[8]  Mark Rosenstein,et al.  Recommending and evaluating choices in a virtual community of use , 1995, CHI '95.

[9]  Ulrich Junker,et al.  QUICKXPLAIN: Preferred Explanations and Relaxations for Over-Constrained Problems , 2004, AAAI.

[10]  David McSherry,et al.  Retrieval Failure and Recovery in Recommender Systems , 2005, Artificial Intelligence Review.

[11]  Prasun Dewan,et al.  Dimensions of tools for detecting software conflicts , 2008, RSSE '08.

[12]  Jakob Nielsen,et al.  Usability engineering , 1997, The Computer Science and Engineering Handbook.

[13]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

[14]  Dana Chisnell,et al.  Handbook of Usability Testing , 2009 .

[15]  Markus Zanker,et al.  A Generic User Modeling Component for Hybrid Recommendation Strategies , 2009, 2009 IEEE Conference on Commerce and Enterprise Computing.

[16]  Alexander Felfernig,et al.  Debugging user interface descriptions of knowledge-based recommender applications , 2006, IUI '06.

[17]  Giuliano Antoniol,et al.  Not all classes are created equal: toward a recommendation system for focusing testing , 2008, RSSE '08.

[18]  Akito Monden,et al.  A recommendation system for software function discovery , 2002, Ninth Asia-Pacific Software Engineering Conference, 2002..

[19]  Dietmar Jannach,et al.  Comparing Recommendation Strategies in a Commercial Context , 2007, IEEE Intelligent Systems.

[20]  Robin Burke,et al.  Knowledge-based recommender systems , 2000 .

[21]  Edward P. K. Tsang,et al.  Foundations of constraint satisfaction , 1993, Computation in cognitive science.

[22]  Manfred Bundschuh,et al.  The IT measurement compendium - estimating and benchmarking success with functional size measurement , 2008 .

[23]  Gerhard Friedrich,et al.  An Integrated Environment for the Development of Knowledge-Based Recommender Applications , 2006, Int. J. Electron. Commer..

[24]  W. Edwards,et al.  Decision Analysis and Behavioral Research , 1986 .

[25]  Markus Zanker,et al.  Recommending Effort Estimation Methods for Software Project Management , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[26]  Heng-Li Yang,et al.  Recommendation system for IT software project planning: A hybrid mining approach for the revised CBR algorithm , 2008, 2008 International Conference on Service Systems and Service Management.

[27]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[28]  Steve McConnell.  Software Estimation: Demystifying the Black Art , 2006 .

[29]  Robin D. Burke,et al.  Hybrid Web Recommender Systems , 2007, The Adaptive Web.

[30]  Markus Zanker,et al.  Case-studies on exploiting explicit customer requirements in recommender systems , 2009, User Modeling and User-Adapted Interaction.

[31]  Markus Zanker,et al.  ISeller: A Flexible Personalization Infrastructure for e-Commerce Applications , 2009, EC-Web.