The Co-evolution of Test Maintenance and Code Maintenance through the Lens of Fine-Grained Semantic Changes

Levin, Stanislav; Yehudai, Amiram

doi:10.1109/icsme.2017.9

Cited by 25 publications

(32 citation statements)

References 39 publications

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“…Our analytical layer has been successfully used to conduct a number of studies in the field of software evolution and maintenance [6][7][8]. In the course of these studies it has processed dozens of millions of records.…”

Section: Discussionmentioning

confidence: 99%

“…In the course of our studies [6][7][8], the data processing stage typically included the following aggregations: commit level; developer level; project level; global statistics. The analytical layer we present allows researchers to produce commit level aggregations (see Listing 5) and obtain statistics such as: change type frequencies, number of test case (test method) addition/removal/modification, number of test suite (test class) addition/removal/modification, associated ticket id, number of test files, and non test files in a given commit.…”

Section: Obtaining Fine Grained Source Code Changesmentioning

confidence: 99%

“…Fine-grained AST level source code changes play an important role in improving our understanding of software maintenance and its evolution [1][2][3][4][5][6][7][8]. In the era of Big Code, researchers seek to study an increasingly large number of repositories to support their findings.…”

Section: Introductionmentioning

confidence: 99%

“…To effectively process large datasets, our analytical layer leverages Apache Spark [12] (henceforth Spark), a widely popular distributed computation engine. The analytical layer we suggest has been successfully used to conduct a number of studies in the field of software maintenance and evolution [6][7][8]. This leads us to believe it can be useful for researchers conducting studies that involve fine-grained source code changes.…”

Section: Introductionmentioning

confidence: 99%

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Processing Large Datasets of Fined Grained Source Code Changes

Levin

Yehudai

2019

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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In the era of Big Code, when researchers seek to study an increasingly large number of repositories to support their findings, the data processing stage may require manipulating millions and more of records.In this work we focus on studies involving fine-grained AST level source code changes. We present how we extended the CodeDistillery source code mining framework with data manipulation capabilities, aimed to alleviate the processing of large datasets of fine grained source code changes. The capabilities we have introduced allow researchers to highly automate their repository mining process and streamline the data acquisition and processing phases. These capabilities have been successfully used to conduct a number of studies, in the course of which dozens of millions of fine-grained source code changes have been processed.

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Section: Discussionmentioning

confidence: 99%

Section: Obtaining Fine Grained Source Code Changesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Processing Large Datasets of Fined Grained Source Code Changes

Levin

Yehudai

2019

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

Self Cite

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“…For Apache Maven build files, we search for pom.xml; for Apache Ant files, we look for build.xml; finally, for Graddle files, we search for build.graddle. Regarding changes on test files, we adapt an heuristic adopted by previous works [16,17]. We classify a change as a test change if the name of the class begins with the word "Test" or ends with the word "Test", or "Tests", or "TestCase".…”

Section: Mining Frequent Code Changes and Association Rulesmentioning

confidence: 99%

How Do Code Changes Evolve in Different Platforms? A Mining-Based Investigation

Viggiato

Oliveira

Figueiredo

et al. 2019

2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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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 changes and applied the Apriori algorithm to find types of changes that frequently co-occur. Our findings show that non-mobile repositories have a higher number of commits per month and our regression models suggest that being mobile significantly impacts on the number of commits in a negative direction when controlling for confound factors, such as code size. We also found that developers do not usually change source code files together with build or test files. We argue that our results can provide valuable information for developers on how changes are performed in different platforms so that practices adopted in successful software systems can be followed.

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Towards automatically identifying the co‐change of production and test code

Huang,

Tang,

Chen

et al. 2024

Software Testing Verif & Rel

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In software evolution, keeping the test code co‐change with the production code is important, because the outdated test code may not work and is ineffective in revealing faults in the production code. However, due to the tight development time, the production and test code may not be co‐changed immediately by developers. For example, we analysed the top 1003 popular Java projects on GitHub and found that nearly 9.3% of cases (i.e., 464,417) did not update their production and test code at the same time, that is, the production code is updated first, and then the test code is updated at intervals. The result indicates that much test code will not be updated in time. In this paper, we propose a novel approach, Jtup, to remind developers to co‐change the production code and test code in time. Specifically, we first define the co‐changed production and test code as a positive instance, while unchanged test code (i.e., production code changed and test code unchanged) as a negative instance. Then, we extract multidimensional features from the production code to characterize the possibility of their co‐change, including code change features, code complexity features, and code semantic features. Finally, several machine learning‐based methods are employed to identify the co‐changed production and test code. We conduct comprehensive experiments on 20 datasets, and the results show that the Accuracy, Precision, and Recall achieved by Jtup are 76.7%, 78.1%, and 77.4%, which outperforms the state‐of‐the‐art method.

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The Co-evolution of Test Maintenance and Code Maintenance through the Lens of Fine-Grained Semantic Changes

Cited by 25 publications

References 39 publications

Processing Large Datasets of Fined Grained Source Code Changes

Processing Large Datasets of Fined Grained Source Code Changes

How Do Code Changes Evolve in Different Platforms? A Mining-Based Investigation

Towards automatically identifying the co‐change of production and test code

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