2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) 2019
DOI: 10.1109/icsme.2019.00033
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How Do Code Changes Evolve in Different Platforms? A Mining-Based Investigation

Abstract: Code changes are performed differently in the mobile and non-mobile platforms. Prior work has investigated the differences in specific platforms. However, we still lack a deeper understanding of how code changes evolve across different software platforms. In this paper, we present a study aiming at investigating the frequency of changes and how source code, build and test changes co-evolve in mobile and non-mobile platforms. We developed regression models to explain which factors influence the frequency of cha… Show more

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Cited by 8 publications
(4 citation statements)
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References 18 publications
(30 reference statements)
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“…This situation is reflected in a work by Knoche et al 33 where the authors introduce DevOps and include it as a choice inside two questions, but it does not remark the importance DevOps has while dealing with microservices. The same circumstance is shown by Viggiato et al 34 where, apart from not making a comprehensive study, DevOps is relegated to a testimonial place when compared to microservices. In contrast, Waseem et al 35 addressed the challenges of employing microservices architectures with DevOps.…”
mentioning
confidence: 64%
“…This situation is reflected in a work by Knoche et al 33 where the authors introduce DevOps and include it as a choice inside two questions, but it does not remark the importance DevOps has while dealing with microservices. The same circumstance is shown by Viggiato et al 34 where, apart from not making a comprehensive study, DevOps is relegated to a testimonial place when compared to microservices. In contrast, Waseem et al 35 addressed the challenges of employing microservices architectures with DevOps.…”
mentioning
confidence: 64%
“…A very high correlation would mean that maintenance effort can be estimated just by using one code metric. This is unrealistic because there are many factors that influence code maintenance, such as developer habits [82], application domain and platforms [88,92], code clones [58], software architecture [4], test code quality [9,78], and changes in requirements.…”
Section: Resultsmentioning
confidence: 99%
“…Insight into the inconsistency of code metric performance: Source code metrics will never model maintenance effort with 100% accuracy, because there are other factors that influence how a code component evolves over time: developer habits (Terceiro et al 2010), application domain and platforms (Zhang et al 2013;Viggiato et al 2019), code clones (Monden et al 2002), software architecture (Aniche et al 2016), and test code quality (Spadini et al 2018;Athanasiou et al 2014). Despite this difficulty, we take a deeper look into the following questions: i) Why is Readability's performance so poor for large methods although it is excellent for all methods (i.e., dominated by small methods)?…”
Section: (Rq4) Evaluating Code Metrics With Contextsmentioning
confidence: 99%