A preliminary investigation of using age and distance measures in the detection of evolutionary couplings

Alali, Abdulkareem; Bartman, Brian; Newman, Christian D.; Maletic, Jonathan I.

doi:10.1109/msr.2013.6624024

Cited by 10 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Co-Change mining is used to predict changes [13], [32], to support program visualization [12], [33], to reveal logical dependencies [34], [35], to improve defect prediction techniques [36], and to detect bad smells [37]. Zimmermann et al propose an approach that uses association rules mining on version histories to suggest possible future changes (e.g., if class A usually co-changes with B, and a commit only changes A, a warning is raised recommending to check whether B should not be changed too) [13].…”

Section: Related Workmentioning

confidence: 99%

Developers' perception of co-change patterns: An empirical study

Silva¹,

Valente²,

Maia³

et al. 2015

2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)

View full text Add to dashboard Cite

Co-change clusters are groups of classes that frequently change together. They are proposed as an alternative modular view, which can be used to assess the traditional decomposition of systems in packages. To investigate developer's perception of co-change clusters, we report in this paper a study with experts on six systems, implemented in two languages. We mine 102 co-change clusters from the version history of such systems, which are classified in three patterns regarding their projection to the package structure: Encapsulated, Crosscutting, and Octopus. We then collect the perception of expert developers on such clusters, aiming to ask two central questions: (a) what concerns and changes are captured by the extracted clusters? (b) do the extracted clusters reveal design anomalies? We conclude that Encapsulated Clusters are often viewed as healthy designs and that Crosscutting Clusters tend to be associated to design anomalies. Octopus Clusters are normally associated to expected class distributions, which are not easy to implement in an encapsulated way, according to the interviewed developers.

show abstract

Section: Related Workmentioning

confidence: 99%

Developers' perception of co-change patterns: An empirical study

Silva¹,

Valente²,

Maia³

et al. 2015

2015 IEEE International Conference on Software Maintenance and Evolution (ICSME)

View full text Add to dashboard Cite

show abstract

“…The clone file proximity between CF and a particular non-predicted co-change candidate is determined by the distance between the corresponding container files in the file system structure as was done by Alali et. al [1]. In case of ranking considering clone type, we provide higher ranks to those non-predicted co-change candidates that are method clones.…”

Section: Answering Research Questionmentioning

confidence: 99%

Prediction and ranking of co-change candidates for clones

Mondal

Roy

Schneider

2014

Proceedings of the 11th Working Conference on Mining Software Repositories

View full text Add to dashboard Cite

Code clones are identical or similar code fragments scattered in a code-base. A group of code fragments that are similar to one another form a clone group. Clones in a particular group often need to be changed together (i.e., co-changed) consistently. However, all clones in a group might not require consistent changes, because some clone fragments might evolve independently. Thus, while changing a particular clone fragment, it is important for a programmer to know which other clone fragments in the same group should be consistently cochanged with that particular clone fragment.In this research work, we empirically investigate whether we can automatically predict and rank these other clone fragments (i.e., the co-change candidates) from a clone group while making changes to a particular clone fragment in this group. For prediction and ranking we automatically retrieve and infer evolutionary coupling among clones by mining the past clone evolution history. Our experimental result on six subject systems written in two different programming languages (C, and Java) considering both exact and near-miss clones implies that we can automatically predict and rank co-change candidates for clones by analyzing evolutionary coupling. Our ranking mechanism can help programmers pinpoint the likely co-change candidates while changing a particular clone fragment and thus, can help us to better manage software clones.

show abstract

“…Nevertheless, we admit that coupling measurement is a complex task that covers diverse ''connection'' forms between software entities (Fregnan et al, 2019). Over the years, different coupling metrics have flourished in the literature, including structural (e.g., Mo et al, 2016;Almugrin et al, 2016;Czibula et al, 2019), dynamic (e.g., Fu and Cai, 2019), semantic (e.g., Czibula et al, 2019), and logical coupling (e.g., Alali et al, 2013). Additionally, in the MSA context, the style of inter-service communication (i.e.…”

Section: Introductionmentioning

confidence: 99%