An investigation of the fault-proneness of clone evolutionary patterns

Barbour, Liliane; An, Le; Khomh, Foutse; Zou, Ying; Wang, Shaohua

doi:10.1007/s11219-017-9375-5

Cited by 10 publications

(3 citation statements)

References 19 publications

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“…Proponents of ''clones being harmful'' believe that code clones can cause clone defects, leading to rise in software maintenance cost. For instance, Barbour et al study the defect-prone of code clones, and find that code clones having inconsistent and divergent patterns are more likely to experience a defect [11]. Mondal et al study the bug-proneness and late propagation tendency of code clones on different clone types, and find that Type-3 clones have the highest bug-proneness among three types of clones [12].…”

Section: Related Workmentioning

confidence: 99%

Improving Maintenance-Consistency Prediction During Code Clone Creation

Zhang¹,

Khoo²,

Su³

2020

IEEE Access

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Developers frequently introduce code clones into software through the copy-and-paste operations during the software development phase in order to shorten development time. Not all such clone creations are beneficial to software maintenance, as they may introduce extra effort at the software maintenance phase, where additional care is needed to ensure consistent change among these clones; i.e., changes made to a piece of code may need to be propagated to other clones. Failure in doing so may risk introducing bugs into software, which are usually called consistent defect. In response to the rampant maintenance cost caused by the introduction of new clones, some researchers have advocated the use of machine-learning approach to predict the likelihood of consistent change requirement when clones are freshly introduced. Leading in this approach is the work by Wang et al., which uses Bayesian Network to model maintenance-consistency of newly introduced clones. In this work, we leverage the success of the above-mentioned work by providing a revised set of attributes that has been shown to strengthen the predictive power of the Bayesian network model, as determined more quantitatively by the precision and recall levels. We firstly provide the definition of clone consistency-maintenance requirement, which can help transfer this problem to a classification problem. Then, based on collecting all clone creation operations through traversing clone genealogies, we redesign the attribute sets for representing clone creation with more information in code and context perspective. We evaluate the effectiveness of our approach on four open source projects with more quantitative analysis, and the experimental results show that our approach possesses a powerful ability in predicting clone consistency. To transfer this work into practice, we develop an Eclipse plug-in tool of this prediction to aid developers in software development and maintenance.INDEX TERMS Code clones, clone consistent change, clone consistency prediction, software maintenance, machine learning.

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Section: Related Workmentioning

confidence: 99%

Improving Maintenance-Consistency Prediction During Code Clone Creation

Zhang¹,

Khoo²,

Su³

2020

IEEE Access

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“…Clone genealogy is the set of states and changes undergone by clone fragments over time that form an evolution history (owing to, eg, a changed clone fragment being left in an inconsistent state). For example, examine clone genealogies to identify the fault‐prone patterns of states and changes, thus enabling that faults to be predicted. Unlike our study, the intention of this research was to explore factors that are correlated with the fault‐proneness of code clones rather than cloning itself.…”

Section: Related Workmentioning

confidence: 99%

“…It should be realized that the implementation and usage of clone tracking mechanisms to determine equivalent clones and track them are not easy and are treated in specific research streams (Duala‐Ekoko et al, 2007,). Although there are some tools that apparently track clones in this way, it is extremely difficult to track actual equivalent clones between releases.…”

Section: Qualitative Analysis and Interpretationmentioning

confidence: 99%

An empirical study on how project context impacts on code cloning

Pérez‐Castillo

Piattini

2018

J Software Evolu Process

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Code cloning can seriously affect software quality. Code clones are various fragments of syntactically or semantically equivalent code. Some authors argue that code clones have a negative impact on maintainability and understandability, since clones propagate defects and make it mandatory to pay attention to several copies. However, other authors believe clones are not necessarily bad, since self‐admitted clones favor system stability and allow developers to move projects forward. Although some root causes and effects of cloning have been widely studied, there is not much relevant work analyzing how certain projects context factors impact on code cloning. This work presents an empirical validation of six open source projects by considering certain factors from Git repositories measured throughout a total of 70 releases for the 6 systems. The factors analyzed were the number of commits and committers per release, the average size of the commits and the size of the system in each release. The main conclusion obtained from the study is that, while the number of commits and committers and the system size do not significantly affect cloning, larger commits lead to a higher cloning ratio. These insights contribute to predicting and preventing code cloning, thus enabling a software quality improvement.

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