poster

A general framework to detect behavioral design patterns

Authors:

Cong Liu,

Boudewijn van Dongen,

Nour Assy,

Wil M. P van der AalstAuthors Info & Claims

ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

Pages 234 - 235

https://doi.org/10.1145/3183440.3194947

Published: 27 May 2018 Publication History

Get Access

Abstract

This paper presents a general framework to detect behavioral design patterns by combining source code and execution data. The framework has been instantiated for the observer, state and strategy patterns to demonstrate its applicability. By experimental evaluation, we show that our combined approach can guarantee a higher precision and recall than purely static approaches. In addition, our approach can discover all missing roles and return complete pattern instances that cannot be supported by existing approaches.

References

[1]

Francesca Arcelli, Fabrizio Perin, Claudia Raibulet, and Stefano Ravani. 2010. Design Pattern Detection in Java Systems: A Dynamic Analysis Based Approach. Evaluation of Novel Approaches to Software Engineering (2010), 163--179.

Google Scholar

[2]

Mario Luca Bernardi, Marta Cimitile, and Giuseppe Di Lucca. 2014. Design pattern detection using a DSL-driven graph matching approach. Journal of Software: Evolution and Process 26, 12(2014), 1233--1266.

Digital Library

Google Scholar

[3]

Andrea De Lucia, Vincenzo Deufemia, Carmine Gravino, and Michele Risi. 2009. Behavioral pattern identification through visual language parsing and code instrumentation. In 13th European Conference on Software Maintenance and Reengineering, CSMR'09. IEEE, 99--108.

Digital Library

Google Scholar

[4]

Cong Liu, Boudewijn van Dongen, Nour Assy, and Wil van der Aalst. 2016. Component Behavior Discovery from Software Execution Data. In International Conference on Computational Intelligence and Data Mining. IEEE, 1--8.

Google Scholar

[5]

Cong Liu, Boudewijn van Dongen, Nour Assy, and Wil van der Aalst. 2018. A Framework to Support Behavioral Design Pattern Detection from Software Execution Data. In 13th International Conference on Evaluation of Novel Approaches to Software Engineering. 1--12.

Google Scholar

[6]

Cong Liu, Boudewijn van Dongen, Nour Assy, and Wil van der Aalst. 2018. Software Architectural Model Discovery from Execution Data. In 13th International Conference on Evaluation of Novel Approaches to Software Engineering. 1--8.

Google Scholar

Cited By

View all

Shankar SAgrawal HE. N(2022)A Survey on Different Approaches to Automating the Design Phase in the Software Development Life CycleResearch Anthology on Agile Software, Software Development, and Testing10.4018/978-1-6684-3702-5.ch027(542-564)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3702-5.ch027
Liu C(2021)A General Framework to Detect Design Patterns by Combining Static and Dynamic Analysis TechniquesInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402140002731:01(21-54)Online publication date: 4-Feb-2021
https://doi.org/10.1142/S0218194021400027
Shankar SAgrawal HE. N(2020)A Survey on Different Approaches to Automating the Design Phase in the Software Development Life CycleHandbook of Research on Engineering Innovations and Technology Management in Organizations10.4018/978-1-7998-2772-6.ch018(350-372)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2772-6.ch018
Show More Cited By

Recommendations

A Framework to Support Behavioral Design Pattern Detection from Software Execution Data
ENASE 2018: Proceedings of the 13th International Conference on Evaluation of Novel Approaches to Software Engineering

The detection of design patterns provides useful insights to help understanding not only the code but also the design and architecture of the underlying software system. Most existing design pattern detection approaches and tools rely on source code as ...
An approach to detect false design patterns
SBCARS '19: Proceedings of the XIII Brazilian Symposium on Software Components, Architectures, and Reuse

A false design pattern occurs when software vocabularies indicate the existence of a design pattern, but it is not implemented. Occurrences of false design patterns may make it difficult to understand, maintain, and evolve software. In this context, we ...
Behavioral specification of GOF design patterns with LOTOS
APSEC '00: Proceedings of the Seventh Asia-Pacific Software Engineering Conference

Software patterns such as gang-of-four (GOF) design patterns are a promising approach for developing software of high quality with less effort. To design larger and complicated software, we often combine design patterns into a larger one, and in this ...

Comments

Information & Contributors

Information

Published In

ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

May 2018

231 pages

ISBN:9781450356633

DOI:10.1145/3183440

Conference Chair:
Michel Chaudron
Chalmers University of Technology, University of Gothenburg, Sweden
,
General Chair:
Ivica Crnkovic
Chalmers University of Technology, University of Gothenburg, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Mark Harman
Facebook and University College London, United Kingdom

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

Check for updates

Author Tags

Qualifiers

Poster

Conference

ICSE '18

Sponsor:

SIGSOFT
IEEE-CS

ICSE '18: 40th International Conference on Software Engineering

May 27 - June 3, 2018

Gothenburg, Sweden

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
161
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Shankar SAgrawal HE. N(2022)A Survey on Different Approaches to Automating the Design Phase in the Software Development Life CycleResearch Anthology on Agile Software, Software Development, and Testing10.4018/978-1-6684-3702-5.ch027(542-564)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3702-5.ch027
Liu C(2021)A General Framework to Detect Design Patterns by Combining Static and Dynamic Analysis TechniquesInternational Journal of Software Engineering and Knowledge Engineering10.1142/S021819402140002731:01(21-54)Online publication date: 4-Feb-2021
https://doi.org/10.1142/S0218194021400027
Shankar SAgrawal HE. N(2020)A Survey on Different Approaches to Automating the Design Phase in the Software Development Life CycleHandbook of Research on Engineering Innovations and Technology Management in Organizations10.4018/978-1-7998-2772-6.ch018(350-372)Online publication date: 2020
https://doi.org/10.4018/978-1-7998-2772-6.ch018
Liu Cvan Dongen BAssy Nvan der Aalst W(2019)Detecting Behavioral Design Patterns from Software Execution DataEvaluation of Novel Approaches to Software Engineering10.1007/978-3-030-22559-9_7(137-164)Online publication date: 29-Jun-2019
https://doi.org/10.1007/978-3-030-22559-9_7
Liu Cvan Dongen BAssy Nvan der Aalst WKhomh FRoy CSiegmund J(2018)Component interface identification and behavioral model discovery from software execution dataProceedings of the 26th Conference on Program Comprehension10.1145/3196321.3196338(97-107)Online publication date: 28-May-2018
https://dl.acm.org/doi/10.1145/3196321.3196338

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

A Framework to Support Behavioral Design Pattern Detection from Software Execution Data

An approach to detect false design patterns

Behavioral specification of GOF design patterns with LOTOS