Cser13.ppt
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This document presents MOFAE, a multi-objective optimization approach to framework API evolution. MOFAE models API evolution as a multi-objective optimization problem. It uses four features - call dependency similarity, method signature text similarity, inheritance tree similarity, and method comment similarity - to recommend adaptation rules. MOFAE selects candidate recommendations that are not dominated in all features and sorts them by how many features they are best in. Evaluation shows MOFAE detects 18% more correct rules than previous works and saves 31% effort.
Cser13.ppt
- 1. MOFAE: Multi-objective Optimization Approach to Framework API Evolution Wei Wu1,2, Yann-Gaël Guéhéneuc1, Giuliano Antoniol2 Ptidej Team1, SOCCER Lab2 DGIGL, École Polytechnique de Montréal, Canada Problem Problem Framework API changes may break the client programs after upgrading Multi-objective Optimization Multi-objective Optimization Output Output Effort Analysis Effort Analysis Search Effort (SE) + Evaluation Effort (EE) Multi-objective optimization (MOOP) is the process of finding solutions to problems with potentially conflicting objectives Smax, a maximum number of tries. Very few frameworks provide adaptation rules #C, Number of correct recommendations |T| - total missing API number The adaptation process is time consuming Evaluation Evaluation Single-recommendation approaches: Existing Approaches Existing Approaches Existing approaches use different features: MOFAE MOFAE Call-dependency SemDiff, Schäfer et al. , Beagle, HiMa, AURA, Halo 1. Recommendation system modeling framework API evolution as a MOOP problem Text-similarity Kim et al., Beagle, AURA, Halo Software design model Diff-CatchUp Software metrics Beagle Comments HiMa, Halo 2. Use four features: call-dependency similarity, method signature text similarity, inheritance tree similarity, and method comment similarity 3. Select the candidates that no other candidates are better than in all the features Multi-recommendation approaches: #Posavg – average correct replacement position #Sizeavg – average recommendation list size Results Results Recommendation list sizes: 4. Sort the selected candidates by in how many features they are the best Objectives Objectives Call dependency similarity Confidence value and support Correct replacement positions: Limitations Limitations Single-feature approaches: Limitations Limitations Semi-automatic Depends on the features Comment similarity Longest Common Subsequence (LCS) Method signature text similarity LCS, Levenshtein Distance (LD), and Method-level Distance (MD) Hybrid approaches: which feature to trust? Inheritance similarity Inheritance tree string LCS p1:P1.p2:P2.p1:PI1.p1:I1.p2:PI2.p2:I2 Conclusion Conclusion Comparison with previous approaches: MOFAE can detect 18% more correct change rules than previous works. Average size 3.7, median size 2.2, maximum size 8. 87% correct recommendations are the first, 99% correct recommendations are in top three. Effort saving 31% Acknowledgements Acknowledgements This work was supported by Fonds québécois de la recherche sur la nature et les technologies and Natural Sciences and Engineering Research Council of Canada