publication . Article . Other literature type . 2019

Leveraging metamorphic testing to automatically detect inconsistencies in code generator families

Boussaa, Mohamed; Barais, Olivier; Sunyé, Gerson; Baudry, Benoit;
Open Access English
  • Published: 20 Dec 2019
  • Publisher: HAL CCSD
  • Country: France
Abstract
International audience; Generative software development has paved the way for the creation of multiple code generators that serve as a basis for automatically generating code to different software and hardware platforms. In this context, the software quality becomes highly correlated to the quality of code generators used during software development. Eventual failures may result in a loss of confidence for the developers, who will unlikely continue to use these generators. It is then crucial to verify the correct behaviour of code generators in order to preserve software quality and reliability. In this paper, we leverage the metamorphic testing approach to auto...
Subjects
free text keywords: test oracle, code generators, metamorphic testing, non-functional properties, software quality, test automation, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Software, Safety, Risk, Reliability and Quality
Funded by
EC| HEADS
Project
HEADS
Heterogeneous and Distributed Services for the Future Computing Continuum
  • Funder: European Commission (EC)
  • Project Code: 611337
  • Funding stream: FP7 | SP1 | ICT
56 references, page 1 of 4

1. Betz T, Cabac L, Güttler M. Improving the development tool chain in the context of petri net-based software development. PNSE: Newcastle, UK, 2011; 167-178.

2. Czarnecki K, Eisenecker UW. Generative programming, 2000. Edited by G. Goos, J. Hartmanis, and J. van Leeuwen, 15.

3. France R, Rumpe B. Model-driven development of complex software: a research roadmap. In 2007 future of software engineering. IEEE Computer Society: Minneapolis, MN, USA, 2007; 37-54. [OpenAIRE]

4. Guana V, Stroulia E. How do developers solve software-engineering tasks on model-based code generators? An empirical study design. First International Workshop on Human Factors in Modeling (HUFAMO 2015). CEUR-WS: Ottawa, Canada, 2015; 33-38. [OpenAIRE]

5. Delgado N, Gates AQ, Roach S. A taxonomy and catalog of runtime software-fault monitoring tools. IEEE Transactions on Software Engineering 2004; 30(12):859-872.

6. Guana V, Stroulia E. Chaintracker, a model-transformation trace analysis tool for code-generation environments. 7th International Conference on Model Transformation (ICMT14): Springer: York, UK, 2014; 146-153. [OpenAIRE]

7. Stuermer I, Conrad M, Doerr H, Pepper P. Systematic testing of model-based code generators. IEEE Transactions on Software Engineering 2007; 33(9):622. [OpenAIRE]

8. Yang X, Chen Y, Eide E, Regehr J. Finding and understanding bugs in c compilers. ACM SIGPLAN Notices, Vol. 46: ACM: San Jose, CA, USA, 2011; 283-294.

9. McKeeman WM. Differential testing for software. Digital Technical Journal 1998; 10(1):100-107.

10. Vouk MA. Back-to-back testing. Information and software technology 1990; 32(1):34-45.

11. Boussaa M, Barais O, Baudry B, Sunyé G. Automatic non-functional testing of code generators families. Proceedings of the 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences: ACM: Amsterdam, Netherlands, 2016; 202-212. [OpenAIRE]

12. Boussaa M. Automatic non-functional testing and tuning of configurable generators. Ph.D. Thesis, Université Rennes 1, 2017. [OpenAIRE]

13. Chae W, Blume M. Building a family of compilers. Software Product Line Conference, 2008. SPLC'08. 12th International, IEEE: Limerick, Ireland, 2008; 307-316.

14. Fumero JJ, Remmelg T, Steuwer M, Dubach C. Runtime code generation and data management for heterogeneous computing in java. Proceedings of the principles and practices of programming on the java platform: ACM: Melbourne, FL, USA, 2015; 16-26. [OpenAIRE]

15. Dasnois B. Haxe 2 Beginner's Guide. Packt Publishing Ltd, 2011.

56 references, page 1 of 4
Abstract
International audience; Generative software development has paved the way for the creation of multiple code generators that serve as a basis for automatically generating code to different software and hardware platforms. In this context, the software quality becomes highly correlated to the quality of code generators used during software development. Eventual failures may result in a loss of confidence for the developers, who will unlikely continue to use these generators. It is then crucial to verify the correct behaviour of code generators in order to preserve software quality and reliability. In this paper, we leverage the metamorphic testing approach to auto...
Subjects
free text keywords: test oracle, code generators, metamorphic testing, non-functional properties, software quality, test automation, [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL], Software, Safety, Risk, Reliability and Quality
Funded by
EC| HEADS
Project
HEADS
Heterogeneous and Distributed Services for the Future Computing Continuum
  • Funder: European Commission (EC)
  • Project Code: 611337
  • Funding stream: FP7 | SP1 | ICT
56 references, page 1 of 4

1. Betz T, Cabac L, Güttler M. Improving the development tool chain in the context of petri net-based software development. PNSE: Newcastle, UK, 2011; 167-178.

2. Czarnecki K, Eisenecker UW. Generative programming, 2000. Edited by G. Goos, J. Hartmanis, and J. van Leeuwen, 15.

3. France R, Rumpe B. Model-driven development of complex software: a research roadmap. In 2007 future of software engineering. IEEE Computer Society: Minneapolis, MN, USA, 2007; 37-54. [OpenAIRE]

4. Guana V, Stroulia E. How do developers solve software-engineering tasks on model-based code generators? An empirical study design. First International Workshop on Human Factors in Modeling (HUFAMO 2015). CEUR-WS: Ottawa, Canada, 2015; 33-38. [OpenAIRE]

5. Delgado N, Gates AQ, Roach S. A taxonomy and catalog of runtime software-fault monitoring tools. IEEE Transactions on Software Engineering 2004; 30(12):859-872.

6. Guana V, Stroulia E. Chaintracker, a model-transformation trace analysis tool for code-generation environments. 7th International Conference on Model Transformation (ICMT14): Springer: York, UK, 2014; 146-153. [OpenAIRE]

7. Stuermer I, Conrad M, Doerr H, Pepper P. Systematic testing of model-based code generators. IEEE Transactions on Software Engineering 2007; 33(9):622. [OpenAIRE]

8. Yang X, Chen Y, Eide E, Regehr J. Finding and understanding bugs in c compilers. ACM SIGPLAN Notices, Vol. 46: ACM: San Jose, CA, USA, 2011; 283-294.

9. McKeeman WM. Differential testing for software. Digital Technical Journal 1998; 10(1):100-107.

10. Vouk MA. Back-to-back testing. Information and software technology 1990; 32(1):34-45.

11. Boussaa M, Barais O, Baudry B, Sunyé G. Automatic non-functional testing of code generators families. Proceedings of the 2016 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences: ACM: Amsterdam, Netherlands, 2016; 202-212. [OpenAIRE]

12. Boussaa M. Automatic non-functional testing and tuning of configurable generators. Ph.D. Thesis, Université Rennes 1, 2017. [OpenAIRE]

13. Chae W, Blume M. Building a family of compilers. Software Product Line Conference, 2008. SPLC'08. 12th International, IEEE: Limerick, Ireland, 2008; 307-316.

14. Fumero JJ, Remmelg T, Steuwer M, Dubach C. Runtime code generation and data management for heterogeneous computing in java. Proceedings of the principles and practices of programming on the java platform: ACM: Melbourne, FL, USA, 2015; 16-26. [OpenAIRE]

15. Dasnois B. Haxe 2 Beginner's Guide. Packt Publishing Ltd, 2011.

56 references, page 1 of 4
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