SH
S. Huang
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Continuous Integration (CI) has become a fundamental practice of modern software engineering, widely adopted in both commercial and open-source projects to enhance development efficiency and software quality. Existing empirical research has examined the relationship between the effectiveness of CI adoption and project characteristics, but it often assumes CI as a uniform intervention and neglects the heterogeneity of its effects. In reality, the CI effectiveness is shaped by contextual factors such as project maturity, domain, programming language, or development practices. Moreover, many prior studies rely on convenience samples of open-source projects, leaving the influence of dataset composition largely unexamined. This study employs quota sampling to construct a diversified dataset, ensuring representation across product characteristics and project activities. Building on this foundation, we examine the adoption of CI both at the aggregate level and within stratified, quotabased subsets of the dataset. The results show that the impact of CI is sensitive to different dataset compositions. When the contextual distribution of projects changes, both the direction and magnitude of CI's effects can shift, emphasizing that empirical evidence on CI effectiveness is inherently context-dependent. These findings indicate that the effectiveness of CI cannot be generalized without considering project heterogeneity. Future research should explicitly control dataset composition to better isolate or explain contextual effects. Besides, applying suitable sampling strategies can also minimize contextual bias, and help generate more robust and generalizable evidence for CI studies.
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Continuous Integration (CI) has become a fundamental practice of modern software engineering, widely adopted in both commercial and open-source projects to enhance development efficiency and software quality. Existing empirical research has examined the relationship between the effectiveness of CI adoption and project characteristics, but it often assumes CI as a uniform intervention and neglects the heterogeneity of its effects. In reality, the CI effectiveness is shaped by contextual factors such as project maturity, domain, programming language, or development practices. Moreover, many prior studies rely on convenience samples of open-source projects, leaving the influence of dataset composition largely unexamined. This study employs quota sampling to construct a diversified dataset, ensuring representation across product characteristics and project activities. Building on this foundation, we examine the adoption of CI both at the aggregate level and within stratified, quotabased subsets of the dataset. The results show that the impact of CI is sensitive to different dataset compositions. When the contextual distribution of projects changes, both the direction and magnitude of CI's effects can shift, emphasizing that empirical evidence on CI effectiveness is inherently context-dependent. These findings indicate that the effectiveness of CI cannot be generalized without considering project heterogeneity. Future research should explicitly control dataset composition to better isolate or explain contextual effects. Besides, applying suitable sampling strategies can also minimize contextual bias, and help generate more robust and generalizable evidence for CI studies.
Configurable Ensembles for Software Similarity
Challenging the Notion of Universal Metrics
Software similarity analysis is crucial in various fields, including code clone detection, security analysis, and software refactoring. While research continues to identify new use cases, numerous similarity detectors have already been proposed for specific contexts. These detectors usually leverage project attributes, such as source code, contributors, documentation, and dependencies. Existing works consistently demonstrate that their approaches outperform others in extensive evaluations. In this paper, we challenge the idea of a universally superior similarity model. We argue that similarity is a fluent concept and that relevant metrics always depend on specific needs. We present a novel framework that enables a flexible aggregation of diverse similarity models, allowing fine-tuned configurations for specific needs and use cases. Our evaluation incorporates multiple existing similarity models and their respective benchmarks to reveal the fundamental dilemma: depending on the configuration, our aggregated model will either confirm prior results or expose significant differences among individual models. However, we will demonstrate that these variations can be explained by the additional information that leads to more fine-grained results. Our results illustrate the future of software similarity research: configurable ensembles of much more specialized models.
...
Software similarity analysis is crucial in various fields, including code clone detection, security analysis, and software refactoring. While research continues to identify new use cases, numerous similarity detectors have already been proposed for specific contexts. These detectors usually leverage project attributes, such as source code, contributors, documentation, and dependencies. Existing works consistently demonstrate that their approaches outperform others in extensive evaluations. In this paper, we challenge the idea of a universally superior similarity model. We argue that similarity is a fluent concept and that relevant metrics always depend on specific needs. We present a novel framework that enables a flexible aggregation of diverse similarity models, allowing fine-tuned configurations for specific needs and use cases. Our evaluation incorporates multiple existing similarity models and their respective benchmarks to reveal the fundamental dilemma: depending on the configuration, our aggregated model will either confirm prior results or expose significant differences among individual models. However, we will demonstrate that these variations can be explained by the additional information that leads to more fine-grained results. Our results illustrate the future of software similarity research: configurable ensembles of much more specialized models.
Numerous studies have shown that Continuous Integration (CI) significantly improves software development productivity. Research has already shown in other fields of software engineering that findings do not always generalize and are often limited to a specific context. So far, research on CI has not differentiated between varying contexts of the studied projects, which includes, for example, varying domains, personnel, technical environments, or cultures. We need to extend the theory of CI by considering the relevant context that will impact how projects approach CI. Although existing studies implicitly touch on context, they often lack a consistent terminology or rely on experience rather than a standardized approach. In this paper, we bridge this gap by developing a taxonomy of relevant contextual factors within the domain of CI. Using grounded theory, we analyze peer-reviewed studies and develop a comprehensive taxonomy of contextual factors of CI that we validate through a practitioner survey. The resulting taxonomy contains multiple levels of details, the main dimensions being Product, Team, Process, Quality, and Scale. The taxonomy offers a structured framework to address the gap in CI research regarding contextual theory. Researchers can use it to describe the scope of findings and to reason about the generalizability of theories. Developers can select and reuse practices more effectively by comparing to other similar projects.
...
Numerous studies have shown that Continuous Integration (CI) significantly improves software development productivity. Research has already shown in other fields of software engineering that findings do not always generalize and are often limited to a specific context. So far, research on CI has not differentiated between varying contexts of the studied projects, which includes, for example, varying domains, personnel, technical environments, or cultures. We need to extend the theory of CI by considering the relevant context that will impact how projects approach CI. Although existing studies implicitly touch on context, they often lack a consistent terminology or rely on experience rather than a standardized approach. In this paper, we bridge this gap by developing a taxonomy of relevant contextual factors within the domain of CI. Using grounded theory, we analyze peer-reviewed studies and develop a comprehensive taxonomy of contextual factors of CI that we validate through a practitioner survey. The resulting taxonomy contains multiple levels of details, the main dimensions being Product, Team, Process, Quality, and Scale. The taxonomy offers a structured framework to address the gap in CI research regarding contextual theory. Researchers can use it to describe the scope of findings and to reason about the generalizability of theories. Developers can select and reuse practices more effectively by comparing to other similar projects.