Exploring the Relationship of a File's History and Its Fault-Proneness: An Empirical Study

Illes-Seifert, Timea; Paech, Barbara

doi:10.1109/taic-part.2008.16

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…For firmware, hot-patching is the ability to patch firmware to a device while maintaining the availability of the system [49]. Finally, Illes-Seifert et al [50] look at hotfixes from a different lens where they consider an update to be a hotfix if it was integrated within the first 5% of time between two consecutive version releases.…”

Section: Domain-specific Definitionmentioning

confidence: 99%

Hot Patching Hot Fixes: Reflection and Perspectives

Hanna,

Petke

2023

2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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With our reliance on software continuously increasing, it is of utmost importance that it be reliable. However, complete prevention of bugs in live systems is unfortunately an impossible task due to time constraints, incomplete testing, and developers not having knowledge of the full stack. As a result, mitigating risks for systems in production through hot patching and hot fixing has become an integral part of software development. In this paper, we first give an overview of the terminology used in the literature for research on this topic. Subsequently, we build upon these findings and present our vision for an automated framework for predicting and mitigating critical software issues at runtime. Our framework combines hot patching and hot fixing research from multiple fields, in particular: software defect and vulnerability prediction, automated test generation and repair, as well as runtime patching. We hope that our vision inspires research collaboration between the different communities.

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Section: Domain-specific Definitionmentioning

confidence: 99%

Hot Patching Hot Fixes: Reflection and Perspectives

Hanna,

Petke

2023

2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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“…Recently, researchers have put more emphasis on process metrics. A considerable process metrics are proposed, mainly including: i) metrics based on code change history, such as number of modified lines [13], [19], [20], [23], [28]- [33], and code relative change metrics [13], [20], [27], [30], ii) metrics based on developer information, such as number of distinct committers [15], [19], [20], [23]- [26], [30], [32], [34], experience of developers [16], [34], commit activities of developers [34], project team organizational structure [17], code ownership [34], and organizational dispersion degree [18], iii) development process related metrics, such as number of revisions [13]- [15], [19], [20], [22], [23], [25], [30], number of defects repaired [30], number of refactorings [20], [30], code change complexity [21], [32], and number of historical defects [19], [23]. The most widely used, classical, and defect-related process metrics are Number of Revisions (NR), Number of Distinct Committers (NDC), Number of Modified Lines (NML), and code relative change metrics.…”

Section: A Process Metricsmentioning

confidence: 99%

“…Graves et al [22] showed that NR was a better predictor of defect, at least better than LOC. Illes-Seifert and Paech [25] compared the correlation between process metrics and the number of defects. The experimental results showed that NR was strongly correlated with the number of defects, and NR had better defect prediction performance.…”

Section: A Process Metricsmentioning

confidence: 99%

“…There are also some researchers who claimed NDC can improve the defect prediction performance. Illes-Seifert and Paech [25] found NDC was highly related to the number of defects, and could obtain great defect prediction performance in terms of predicting the number of defects. Matsumoto et al [26] analyzed the correlation between a variety of developer metrics and the number of defects, and evaluated the influence of developer metrics on the defect prediction performance with the number of defects as prediction target.…”

Section: A Process Metricsmentioning

confidence: 99%

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Which Process Metrics Are Significantly Important to Change of Defects in Evolving Projects: An Empirical Study

Jiang

Gong

et al. 2020

IEEE Access

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Process metrics can reflect the software development process and the code changes which are the main causes of defects. So, recently, the researches have put more emphasis on process metrics in the field of software defect prediction. For evolving projects, it is more meaningful to study whether the software module introduces or eliminates defects or not, not whether the software module is defective or defect-free. However, no such work is available in the literature focusing on the change of defect state. Discovering the factors that influence the change of defect state in the process of software development can help us to understand the causes of software defects and improve the quality of subsequent software versions. Therefore, this paper presents an extensive empirical study on which process metrics are significantly important to change of defects in evolving projects. Five process metrics of 37 versions in 12 software projects are collected. We not only analyze the class correlation values and the classification performance values among five process metrics, but also perform statistical analysis to verify whether the experimental results are of practical value. The experimental results indicate that Number of Distinct Committers plays a significantly important role in the change of defect state, especially for elimination of defects, and Number of Revisions is the second, whereas Degree of Code Modification is the last. In addition, Average Number of Modified Lines is superior to Number of Modified Lines. Based on the experimental results, some suggestions for software development and software defect prediction are also discussed.

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Integration Test Order Strategies to Consider Test Focus and Simulation Effort

Borner¹,

Paech

2009

2009 First International Conference on Advances in System Testing and Validation Lifecycle

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Exploring the Relationship of a File's History and Its Fault-Proneness: An Empirical Study

Cited by 4 publications

References 11 publications

Hot Patching Hot Fixes: Reflection and Perspectives

Hot Patching Hot Fixes: Reflection and Perspectives

Which Process Metrics Are Significantly Important to Change of Defects in Evolving Projects: An Empirical Study

Integration Test Order Strategies to Consider Test Focus and Simulation Effort

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