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Evaluation of Accuracy in Design Pattern Occurrence Detection

Published: 01 July 2010 Publication History

Abstract

Detection of design pattern occurrences is part of several solutions to software engineering problems, and high accuracy of detection is important to help solve the actual problems. The improvement in accuracy of design pattern occurrence detection requires some way of evaluating various approaches. Currently, there are several different methods used in the community to evaluate accuracy. We show that these differences may greatly influence the accuracy results, which makes it nearly impossible to compare the quality of different techniques. We propose a benchmark suite to improve the situation and a community effort to contribute to, and evolve, the benchmark suite. Also, we propose fine-grained metrics assessing the accuracy of various approaches in the benchmark suite. This allows comparing the detection techniques and helps improve the accuracy of detecting design pattern occurrences.

Cited By

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  • (2022)Design patterns mining using neural sub-graph matchingProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507073(1545-1553)Online publication date: 25-Apr-2022
  • (2022)A new method for detecting various variants of GoF design patterns using conceptual signaturesSoftware Quality Journal10.1007/s11219-021-09576-930:3(651-686)Online publication date: 1-Sep-2022
  • (2020)A preliminary analysis of self-adaptive systems according to different issuesSoftware Quality Journal10.1007/s11219-020-09502-528:3(1213-1243)Online publication date: 1-Sep-2020
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Published In

cover image IEEE Transactions on Software Engineering
IEEE Transactions on Software Engineering  Volume 36, Issue 4
July 2010
142 pages

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IEEE Press

Publication History

Published: 01 July 2010

Author Tags

  1. Patterns
  2. Patterns, object-oriented design methods, measurement techniques, evaluation, reverse engineering, reengineering, restructuring.
  3. evaluation
  4. measurement techniques
  5. object-oriented design methods
  6. reengineering
  7. restructuring.
  8. reverse engineering

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Cited By

View all
  • (2022)Design patterns mining using neural sub-graph matchingProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507073(1545-1553)Online publication date: 25-Apr-2022
  • (2022)A new method for detecting various variants of GoF design patterns using conceptual signaturesSoftware Quality Journal10.1007/s11219-021-09576-930:3(651-686)Online publication date: 1-Sep-2022
  • (2020)A preliminary analysis of self-adaptive systems according to different issuesSoftware Quality Journal10.1007/s11219-020-09502-528:3(1213-1243)Online publication date: 1-Sep-2020
  • (2018)A model for analysis and presentation of design pattern detection resultsProceedings of the 33rd Annual ACM Symposium on Applied Computing10.1145/3167132.3167292(1500-1509)Online publication date: 9-Apr-2018
  • (2017)Design pattern detection based on the graph theoryKnowledge-Based Systems10.1016/j.knosys.2017.01.007120:C(211-225)Online publication date: 15-Mar-2017
  • (2016)Survey On Software Design-Pattern Specification LanguagesACM Computing Surveys10.1145/292696649:1(1-35)Online publication date: 29-Jun-2016
  • (2015)Identification of simulink model antipattern instances using model clone detectionProceedings of the 18th International Conference on Model Driven Engineering Languages and Systems10.5555/3351736.3351770(276-285)Online publication date: 30-Sep-2015
  • (2015)On applying machine learning techniques for design pattern detectionJournal of Systems and Software10.1016/j.jss.2015.01.037103:C(102-117)Online publication date: 1-May-2015
  • (2015)A comprehensive approach to the recovery of design pattern instances based on sub-patterns and method signaturesJournal of Systems and Software10.1016/j.jss.2015.01.019103:C(1-16)Online publication date: 1-May-2015
  • (2015)Source code and design conformance, design pattern detection from source code by classification approachApplied Soft Computing10.1016/j.asoc.2014.10.02726:C(357-367)Online publication date: 1-Jan-2015
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