Causes of Architecture Changes: An Empirical Study through the Communication in OSS Mailing Lists

Ding, Wei; Liang, Peng; Tang, Antony; Vliet, Hans van

doi:10.18293/seke2015-193

Cited by 5 publications

(10 citation statements)

References 29 publications

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“…A few of the studies [8], [29] created datasets for architectural change classification from the development history. The recent dataset created by Mondal et al [29] consists of 362 samples of four projects (26 of them are adaptive).…”

Section: Dataset Preparationmentioning

confidence: 99%

“…Development team can group architectural changes leveraging change classification process based on the cause of the change, type of change, location of the change, the size of the code modification, and impact of change [40], [15]. For example, four major causes of architectural changes have been defined explicitly in the literature [40], [8], [29]: (i) perfective DOI reference number: 10.18293/SEKE2021-060 -adjusting new behaviour, (ii) preventive -prevent bad design, (iii) corrective -correct discovered problems, and (iv) adaptive -adapting to new platform.…”

Section: Introductionmentioning

confidence: 99%

“…Grouping causes of change is beneficial for post-release analyses, where design change activities are not explicitly annotated [8]. Change classification is also required for composing a developer's profile, building a balanced team, and handling anomalies in the development process [21].…”

Section: Introductionmentioning

confidence: 99%

“…Yet, supporting architectural change classification is still in its infancy [29], [11], perhaps, due to lack of benchmark data and requirements of laborious human analysis. Nevertheless, a few of the studies explored for both manual [8], [32] and semi-automated [29] techniques for classifying architectural changes. In these studies, a small collection of samples is being experimented where challenges are not identified properly, which leads to developing infeasible models.…”

Section: Introductionmentioning

confidence: 99%

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ArchiNet: A Concept-token based Approach for Determining Architectural Change Categories

Kumar¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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Causes of software architectural change are classified as perfective, preventive, corrective, and adaptive. Change classification is used to promote common approaches for addressing similar changes, produce appropriate design documentation for a release, construct a developer's profile, form a balanced team, support code review, etc. However, automated architectural change classification techniques are in their infancy, perhaps due to the lack of a benchmark dataset and the need for extensive human involvement. To address these shortcomings, we present a benchmark dataset and a text classifier for determining the architectural change rationale from commit descriptions. First, we explored source code properties for change classification independent of project activity descriptions and found poor outcomes. Next, through extensive analysis, we identified the challenges of classifying architectural change from text and proposed a new classifier that uses concept tokens derived from the concept analysis of change samples. We also studied the sensitivity of change classification of various types of tokens present in commit messages. The experimental outcomes employing 10fold and cross-project validation techniques with five popular open-source systems show that the F1 score of our proposed classifier is around 70%. The precision and recall are mostly consistent among all categories of change and more promising than competing methods for text classification.

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Section: Dataset Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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ArchiNet: A Concept-token based Approach for Determining Architectural Change Categories

Kumar¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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“…The results show that the major cause for architecture changes in both Hibernate and ArgoUML is preventative changes, which ease future maintenance by restructuring or reengineering the system. A further analysis of architecture change cause based on the collected architectural posts using the coding techniques of grounded theory, and a detailed description and explanation of the study process and data analysis in the methodology part, are the major extensions to our previous work presented in [1].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Causes of Architecture Changes Using OSS Mailing Lists

Ding

Liang

Tang

et al. 2015

Int. J. Soft. Eng. Knowl. Eng.

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The causes of architecture changes can tell about why architecture changes, and this knowledge can be captured to prevent architecture knowledge vaporization and architecture degeneration. But the causes are not always known, especially in open source software (OSS) development. This makes it very hard to understand the underlying reasons for the architecture changes and design appropriate modifications. Architecture information is communicated in development mailing lists of OSS projects. To explore the possibility of identifying and understanding the causes of architecture changes, we conducted an empirical study to analyze architecture information (i.e., architectural threads) communicated in the development mailing lists of two popular OSS projects: Hibernate and ArgoUML, verified architecture changes with source code, and identified the causes of architecture changes from the communicated architecture information. The main findings of this study are: (1) architecture information communicated in OSS mailing lists does lead to architecture changes in code; (2) the major cause for architecture changes in both Hibernate and ArgoUML is preventative changes, and the causes of architecture changes are further classified to functional requirement, external quality requirement, and internal quality requirement using the coding techniques of grounded theory; (3) more than 45% of architecture changes in both projects happened before the first stable version was released. * This paper is an enhanced version of our previous paper "Causes of Architecture Changes: An Empirical

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An Exploratory Study on Automatic Architectural Change Analysis Using Natural Language Processing Techniques

Mondal

Roy

Schneider

2019

2019 19th International Working Conference on Source Code Analysis and Manipulation (SCAM)

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Causes of Architecture Changes: An Empirical Study through the Communication in OSS Mailing Lists

Cited by 5 publications

References 29 publications

ArchiNet: A Concept-token based Approach for Determining Architectural Change Categories

ArchiNet: A Concept-token based Approach for Determining Architectural Change Categories

Understanding the Causes of Architecture Changes Using OSS Mailing Lists

An Exploratory Study on Automatic Architectural Change Analysis Using Natural Language Processing Techniques

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