Identification of Move Method Refactoring Opportunities

Tsantalis, Nikolaos; Chatzigeorgiou, Alexander

doi:10.1109/tse.2009.1

Cited by 307 publications

(238 citation statements)

References 23 publications

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“…We chose not to use existing detection tools (Marinescu 2004;Khomh et al 2009b;Sahin et al 2014;Tsantalis and Chatzigeorgiou 2009;Moha et al 2010;Oliveto et al 2010;Palomba et al 2015a) because (i) none of them has ever been applied to detect all the studied code smells and (ii) their detection rules are generally more restrictive to ensure a good compromise between recall and precision and thus may miss some smell instances. To verify this claim, we evaluated the behavior of three existing tools, i.e., DECOR (Moha et al 2010), JDeodorant (Tsantalis and Chatzigeorgiou 2009), and HIST (Palomba et al 2015a) on one of the systems used in the empirical study, i.e., Apache Cassandra 1.1.…”

Section: Research Questions and Planningmentioning

confidence: 99%

“…To verify this claim, we evaluated the behavior of three existing tools, i.e., DECOR (Moha et al 2010), JDeodorant (Tsantalis and Chatzigeorgiou 2009), and HIST (Palomba et al 2015a) on one of the systems used in the empirical study, i.e., Apache Cassandra 1.1. When considering the God Class smell none of the available tools is able to identify all the eight actual smell instances we found by manually analyzing the classes of this system.…”

Section: Research Questions and Planningmentioning

confidence: 99%

“…On the one side, researchers developed methods and tools to detect code smells. Such tools exploit different types of approaches, including metrics-based detection (Lanza and Marinescu 2010;Moha et al 2010;Marinescu 2004;Munro 2005), graph-based techniques (Tsantalis and Chatzigeorgiou 2009), mining of code changes (Palomba et al 2015a), textual analysis of source code (Palomba et al 2016b), or search-based optimization techniques (Kessentini et al 2010;Sahin et al 2014). On the other side, researchers investigated how relevant code smells are for developers (Yamashita and Moonen 2013;Palomba et al 2014), when and why they are introduced (Tufano et al 2015), how they evolve over time (Arcoverde et al 2011;Chatzigeorgiou and Manakos 2010;Lozano et al 2007;Ratiu et al 2004;Tufano et al 2017), and whether they impact on software quality properties, such as program comprehensibility (Abbes et al 2011), fault-and change-proneness (Khomh et al 2012;Khomh et al 2009a;D'Ambros et al 2010), and code maintainability Moonen 2012, 2013;Deligiannis et al 2004;Li and Shatnawi 2007;Sjoberg et al 2013).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation

et al. 2017

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Code smells are symptoms of poor design and implementation choices that may hinder code comprehensibility and maintainability. Despite the effort devoted by the research community in studying code smells, the extent to which code smells in software systems affect software maintainability remains still unclear. In this paper we present a large scale empirical investigation on the diffuseness of code smells and their impact on code change-and fault-proneness. The study was conducted across a total of 395 releases of 30

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Section: Research Questions and Planningmentioning

confidence: 99%

Section: Research Questions and Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation

et al. 2017

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“…The research community has been also actively developing approaches and tools for detecting smells [11], [34], [37], [44], [33], and, whenever possible, triggering refactoring operations. Such tools rely on different types of analysis techniques, such as constraint-based reasoning over metric values [33], [34], static code analysis [44], or analysis of software changes [37].…”

Section: Introductionmentioning

confidence: 99%

“…Such tools rely on different types of analysis techniques, such as constraint-based reasoning over metric values [33], [34], static code analysis [44], or analysis of software changes [37]. While these tools provide relatively accurate and complete identification of a wide variety of smells, most of them work by "taking a snapshot" of the system or by looking at recent changes, hence providing a snapshot-based recommendation to the developer.…”

Section: Introductionmentioning

confidence: 99%

When and Why Your Code Starts to Smell Bad

Tufano

Palomba

Bavota

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

206

143

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Abstract-In past and recent years, the issues related to managing technical debt received significant attention by researchers from both industry and academia. There are several factors that contribute to technical debt. One of these is represented by code bad smells, i.e., symptoms of poor design and implementation choices. While the repercussions of smells on code quality have been empirically assessed, there is still only anecdotal evidence on when and why bad smells are introduced. To fill this gap, we conducted a large empirical study over the change history of 200 open source projects from different software ecosystems and investigated when bad smells are introduced by developers, and the circumstances and reasons behind their introduction. Our study required the development of a strategy to identify smellintroducing commits, the mining of over 0.5M commits, and the manual analysis of 9,164 of them (i.e., those identified as smellintroducing). Our findings mostly contradict common wisdom stating that smells are being introduced during evolutionary tasks. In the light of our results, we also call for the need to develop a new generation of recommendation systems aimed at properly planning smell refactoring activities.

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Investigating code quality tools in the context of software engineering education

Silva

Nunes

Terra

2017

Comp Applic In Engineering

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A key issue involved with software engineering education consists of how to guarantee that adequate software engineering principles are being followed at the code level, thus reinforcing that students produce high‐quality code. Reviewing and grading student projects to verify whether they followed such principles is a time‐consuming task, since this typically involves manual code inspection. In this paper, we exploit code quality tools and metrics to automatically assess student projects with respect to methods with many responsibilities (i.e., where the Extract Method refactoring should be applied), and evaluate their effectiveness. We conducted a study using two sets of student projects, developed in two academic semesters. Our results indicate that, to reduce the effort required to grade projects, two traditional code metrics, namely method lines of code and number of statements, perform best, and other metrics can be selected according to the system being implemented. © 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:230–241, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21793

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Identification of Move Method Refactoring Opportunities

Cited by 307 publications

References 23 publications

On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation

On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation

When and Why Your Code Starts to Smell Bad

Investigating code quality tools in the context of software engineering education

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