A Feature-Similarity Model for Product Line Engineering

Search, retrieval and comparison of products in a product line are common tasks during product line evolution. Feature modeling approaches do not easily support these tasks. This vision paper sets out a proposal for a feature-similarity model in which similarity metrics as used for example in case-based reasoning (CBR) are integrated with feature models. We describe potential applications for Product Line Scoping, Domain Engineering and Application Engineering.

[1]  Adam Czyszczon,et al.  The MapReduce Approach to Web Service Retrieval , 2013, ICCCI.

[2]  Hermann Kaindl,et al.  An Approach to Method-Tool Coupling for Software Development , 2010, 2010 Fifth International Conference on Software Engineering Advances.

[3]  Deborah G. Ancona,et al.  6 The Functional Perspective , 2005 .

[4]  Hermann Kaindl,et al.  On confusion between requirements and their representations , 2010, Requirements Engineering.

[5]  Gerhard Austaller Service Discovery , 2008, Handbook of Research on Ubiquitous Computing Technology for Real Time Enterprises.

[6]  Daniel Bildhauer,et al.  Similarity-driven software reuse , 2009, 2009 ICSE Workshop on Comparison and Versioning of Software Models.

[7]  Hermann Kaindl,et al.  Case-based Reuse with Partial Requirements Specifications , 2010, 2010 18th IEEE International Requirements Engineering Conference.

[8]  Linda M. Northrop,et al.  A Framework for Software Product Line Practice , 1999, ECOOP Workshops.

[9]  Jörg Becker,et al.  An Ontology-Based Natural Language Service Discovery Engine - Design and Experimental Evaluation , 2010, ECIS.

[10]  Yuanyuan Zhang,et al.  Search based software engineering for software product line engineering: a survey and directions for future work , 2014, SPLC.

[11]  Alexander Felfernig,et al.  Automated Analysis in Feature Modelling and Product Configuration , 2013, ICSR.

[12]  Michael Eisenbarth,et al.  A decade of scoping: a survey , 2009, SPLC.

[13]  Tadeusz M. Szuba,et al.  Computational Collective Intelligence , 2001, Lecture Notes in Computer Science.

[14]  M ANNL.,et al.  Alignable and nonalignable differences in causal explanations , 2005 .

[15]  Matthine Klusch,et al.  Semantic Web Service Coordination , 2008 .

[16]  Iris Reinhartz-Berger,et al.  Generating feature models from requirements: structural vs. functional perspectives , 2014, SPLC '14.

[17]  Ruzanna Chitchyan,et al.  A framework for constructing semantically composable feature models from natural language requirements , 2009, SPLC.

[18]  John F Av Aro,et al.  Safe and Secure Software Reuse , 2013, Lecture Notes in Computer Science.

[19]  Hermann Kaindl,et al.  Using similarity metrics for mining variability from software repositories , 2014, SPLC '14.

[20]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.