RePOR: Mimicking humans on refactoring tasks. Are we there yet?
Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are “poor” solutions to recur...
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| Vydáno v: | Empirical software engineering : an international journal Ročník 25; číslo 4; s. 2960 - 2996 |
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01.07.2020
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| ISSN: | 1382-3256, 1573-7616 |
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| Abstract | Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are “poor” solutions to recurring design problems. However, little quantitative evidence exists about the impact of automatically refactored code on program comprehension, and in which context automated refactoring can be as effective as manual refactoring. Leveraging RePOR, an automated refactoring approach based on partial order reduction techniques, we performed an empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks. (1) We surveyed 80 developers, asking them to identify from a set of 20 refactoring changes if they were generated by developers or by a tool, and to rate the refactoring changes according to their design quality; (2) we asked 30 developers to complete code comprehension tasks on 10 systems that were refactored by either a freelancer or an automated refactoring tool. To make comparison fair, for a subset of refactoring actions that introduce new code entities, only synthetic identifiers were presented to practitioners. We measured developers’ performance using the NASA task load index for their effort, the time that they spent performing the tasks, and their percentages of correct answers. Our findings, despite current technology limitations, show that it is reasonable to expect a refactoring tools to match developer code. Indeed, results show that for 3 out of the 5 anti-pattern types studied, developers could not recognize the origin of the refactoring (i.e., whether it was performed by a human or an automatic tool). We also observed that developers do not prefer human refactorings over automated refactorings, except when refactoring Blob classes; and that there is no statistically significant difference between the impact on code understandability of human refactorings and automated refactorings. We conclude that automated refactorings can be as effective as manual refactorings. However, for complex anti-patterns types like the Blob, the perceived quality achieved by developers is slightly higher. |
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| AbstractList | Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are “poor” solutions to recurring design problems. However, little quantitative evidence exists about the impact of automatically refactored code on program comprehension, and in which context automated refactoring can be as effective as manual refactoring. Leveraging RePOR, an automated refactoring approach based on partial order reduction techniques, we performed an empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks. (1) We surveyed 80 developers, asking them to identify from a set of 20 refactoring changes if they were generated by developers or by a tool, and to rate the refactoring changes according to their design quality; (2) we asked 30 developers to complete code comprehension tasks on 10 systems that were refactored by either a freelancer or an automated refactoring tool. To make comparison fair, for a subset of refactoring actions that introduce new code entities, only synthetic identifiers were presented to practitioners. We measured developers’ performance using the NASA task load index for their effort, the time that they spent performing the tasks, and their percentages of correct answers. Our findings, despite current technology limitations, show that it is reasonable to expect a refactoring tools to match developer code. Indeed, results show that for 3 out of the 5 anti-pattern types studied, developers could not recognize the origin of the refactoring (i.e., whether it was performed by a human or an automatic tool). We also observed that developers do not prefer human refactorings over automated refactorings, except when refactoring Blob classes; and that there is no statistically significant difference between the impact on code understandability of human refactorings and automated refactorings. We conclude that automated refactorings can be as effective as manual refactorings. However, for complex anti-patterns types like the Blob, the perceived quality achieved by developers is slightly higher. |
| Author | Khomh, Foutse Morales, Rodrigo Antoniol, Giuliano |
| Author_xml | – sequence: 1 givenname: Rodrigo orcidid: 0000-0002-1350-0560 surname: Morales fullname: Morales, Rodrigo email: rodrigomorales2@acm.org organization: Department of Computer Science and Software Engineering, Concordia University – sequence: 2 givenname: Foutse surname: Khomh fullname: Khomh, Foutse organization: Département de génie informatique et génie logiciel, École Polytechnique de Montréal – sequence: 3 givenname: Giuliano surname: Antoniol fullname: Antoniol, Giuliano organization: Département de génie informatique et génie logiciel, École Polytechnique de Montréal |
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| Keywords | Program Comprehension Anti-patterns Software maintenance Empirical software engineering studies Refactoring |
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| References_xml | – reference: Beck K, Andres C (2004) Extreme programming explained: Embrace change, 2nd edon. Addison-Wesley Professional – reference: Romano J, Kromrey JD, Coraggio J, Skowronek J, Devine L (2006) Exploring methods for evaluating group differences on the nsse and other surveys: Are the t-test and cohen’sd indices the most appropriate choices. In: Annual meeting of the southern association for institutional research – reference: Opdyke WF (1992) Refactoring object-oriented frameworks. Ph.D. thesis., University of Illinois at Urbana-Champaign – reference: Brown WJ, Malveau RC, Brown WH, Mccormick III HW, Mowbray TJ (1998) Anti Patterns: Refactoring Software, Architectures, and Projects in Crisis, 1st edn. Wiley, New York – reference: Hassan AE (2009) Predicting faults using the complexity of code changes. In: 2009 IEEE 31St international conference on software engineering, pp 78–88. https://doi.org/10.1109/ICSE.2009.5070510 – reference: Le Goues C, Dewey-Vogt M, Forrest S, Weimer W (2012) A systematic study of automated program repair: Fixing 55 out of 105 bugs for $8 each. In: 2012 34th international conference on Software engineering (ICSE). IEEE, pp 3–13 – reference: MoralesRSohZKhomhFAntoniolGChicanoFOn the use of developers’ context for automatic refactoring of software anti-patternsJ Syst Softw201712823625110.1016/j.jss.2016.05.042 – reference: Wohlin C, Runeson P, Höst M, Ohlsson MC, Regnell B, Wesslén A (2012) Experimentation in software engineering. Springer Science & Business Media – reference: Vakilian M, Chen N, Negara S, Rajkumar BA, Bailey BP, Johnson RE (2012) Use, disuse, and misuse of automated refactorings. In: 2012 34th international conference on Software engineering (ICSE). 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