How Does Library Migration Impact Software Quality and Comprehension? An Empirical Study

Alrubaye, Hussein; Alshoaibi, Deema; AlOmar, Eman Abdullah; Mkaouer, Mohamed Wiem; Ouni, Ali

doi:10.1007/978-3-030-64694-3_15

Cited by 17 publications

(6 citation statements)

References 46 publications

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“…Terms such as 'readability', 'efficiency', and 'performance' represent the developers' main focus, with 16.37%, 13.85%, and 11.52%, respectively. Although multiple studies [75,53,32] have been analyzing code comprehension and using metrics to measure readability, there is no mention of these readability tools/models (i.e., [50,89,46,83]) in the questions. For instance, developers refactor the code to improve its reusability.…”

Section: Resultsmentioning

confidence: 99%

How Do I Refactor This? An Empirical Study on Refactoring Trends and Topics in Stack Overflow

Peruma,

Simmons,

AlOmar

et al. 2021

Preprint

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An essential part of software maintenance and evolution, refactoring is performed by developers, regardless of technology or domain, to improve the internal quality of the system, and reduce its technical debt. However, choosing the appropriate refactoring strategy is not always straightforward, resulting in developers seeking assistance. Although research in refactoring is well-established, with several studies altering between the detection of refactoring opportunities and the recommendation of appropriate code changes, little is known about their adoption in practice. Analyzing the perception of developers is critical to understand better what developers consider to be problematic in their code and how they handle it. Additionally, there is a need for bridging the gap between refactoring, as research, and its adoption in practice, by extracting common refactoring intents that are more suitable for what developers face in reality.In this study, we analyze refactoring discussions on Stack Overflow through a series of quantitative and qualitative experiments. Our results show that Stack Overflow is utilized by a diverse set of developers for refactoring assistance for a variety of technologies. Our observations show five areas that developers typically require help with refactoring-Code Optimization, Tools and IDEs, Architecture and Design Patterns, Unit Testing, and Database. We envision our findings better bridge the support between traditional (or academic) aspects of refactoring and their real-world applicability, including better tool support.

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Section: Resultsmentioning

confidence: 99%

How Do I Refactor This? An Empirical Study on Refactoring Trends and Topics in Stack Overflow

Peruma,

Simmons,

AlOmar

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Alrubaye et al [1] conducted an empirical study to highlight the benefits of the migration process over software quality measured by the three standard metrics used in the domain, i.e., coupling, cohesion, and complexity. By relying on a dataset composed of nine different libraries and 57,447 Java projects, statistical tests have been carried on relevant migration data.…”

Section: Related Workmentioning

confidence: 99%

Providing Upgrade Plans for Third-party Libraries: A Recommender System using Migration Graphs

Rubei¹,

Ruscio²,

Sipio³

et al. 2022

Preprint

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During the development of a software project, developers often need to upgrade third-party libraries (TPLs), aiming to keep their code up-to-date with the newest functionalities offered by the used libraries. In most cases, upgrading used TPLs is a complex and error-prone activity that must be carefully carried out to limit the ripple effects on the software project that depends on the libraries being upgraded. In this paper, we propose EvoPlan as a novel approach to the recommendation of different upgrade plans given a pair of library-version as input. In particular, among the different paths that can be possibly followed to upgrade the current library version to the desired updated one, EvoPlan is able to suggest the plan that can potentially minimize the efforts being needed to migrate the code of the clients from the library's current release to the target one. The approach has been evaluated on a curated dataset using conventional metrics used in Information Retrieval, i.e., precision, recall, and F-measure. The experimental results show that EvoPlan obtains an encouraging prediction performance considering two

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“…These metrics have been previously used to assess the quality of refactoring or to recommend refactorings [3,[49][50][51]. In addition to that, many previous papers have found significant correlation code metrics and refactoring [11,13,52]. Their findings show that metrics can be a strong indicator for refactoring activity, regardless of whether it improves or degrades these metric values.…”

Section: Metric-based Modelmentioning

confidence: 99%

“…[10,11] and quality attributes (e.g., reuse, etc.) [12,13]. Commit messages were also used by Rebai et al [14] to recommend refactoring operations.…”

Section: Introductionmentioning

confidence: 99%

Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities

et al. 2021

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Understanding how developers refactor their code is critical to support the design improvement process of software. This paper investigates to what extent code metrics are good indicators for predicting refactoring activity in the source code. In order to perform this, we formulated the prediction of refactoring operation types as a multi-class classification problem. Our solution relies on measuring metrics extracted from committed code changes in order to extract the corresponding features (i.e., metric variations) that better represent each class (i.e., refactoring type) in order to automatically predict, for a given commit, the method-level type of refactoring being applied, namely Move Method, Rename Method, Extract Method, Inline Method, Pull-up Method, and Push-down Method. We compared various classifiers, in terms of their prediction performance, using a dataset of 5004 commits and extracted 800 Java projects. Our main findings show that the random forest model trained with code metrics resulted in the best average accuracy of 75%. However, we detected a variation in the results per class, which means that some refactoring types are harder to detect than others.

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How Does Library Migration Impact Software Quality and Comprehension? An Empirical Study

Cited by 17 publications

References 46 publications

How Do I Refactor This? An Empirical Study on Refactoring Trends and Topics in Stack Overflow

How Do I Refactor This? An Empirical Study on Refactoring Trends and Topics in Stack Overflow

Providing Upgrade Plans for Third-party Libraries: A Recommender System using Migration Graphs

Comparing Commit Messages and Source Code Metrics for the Prediction Refactoring Activities

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