Do Internal Software Metrics Have Relationship with Fault-proneness and Change-proneness?

• URL: http://arxiv.org/abs/2310.03673v2
• Date: Fri, 7 Jun 2024 18:19:56 GMT
• Title: Do Internal Software Metrics Have Relationship with Fault-proneness and Change-proneness?
• Authors: Md. Masudur Rahman, Toukir Ahammed, Kazi Sakib,
• Abstract summary: We identified 25 internal software metrics along with the measures of change-proneness and fault-proneness within the Apache and Eclipse ecosystems. Most of the metrics have little to no correlation with fault-proneness. metrics related to inheritance, coupling, and comments showed a moderate to high correlation with change-proneness.
• Score: 1.9526430269580959
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fault-proneness is a measure that indicates the possibility of programming errors occurring within a software system. On the other hand, change-proneness refers to the potential for modifications to be made to the software. Both of these measures are crucial indicators of software maintainability, as they influence internal software metrics such as size, inheritance, and coupling, particularly when numerous changes are made to the system. In the literature, research has predicted change- and fault-proneness using internal software metrics that is almost a decade old. However, given the continuous evolution of software systems, it is essential to revisit and update our understanding of these relationships. Therefore, we have conducted an empirical study to revisit the relationship between internal software metrics and change-proneness, and faultproneness, aiming to provide current and relevant insights. In our study, we identified 25 internal software metrics along with the measures of change-proneness and fault-proneness within the wellknown open-source systems from the Apache and Eclipse ecosystems. We then analyzed the relationships between these metrics using statistical correlation methods. Our results revealed that most of the metrics have little to no correlation with fault-proneness. However, metrics related to inheritance, coupling, and comments showed a moderate to high correlation with change-proneness. These findings will assist developers to minimize the higher correlated software metrics to enhance maintainability in terms of change- and fault-proneness. Additionally, these insights can guide researchers in developing new approaches for predicting changes and faults by incorporating the metrics that have been shown to have stronger correlations.

Related papers

• Comprehensive Equity Index (CEI): Definition and Application to Bias Evaluation in Biometrics [47.762333925222926]
  We present a novel metric to quantify biased behaviors of machine learning models. We focus on and apply it to the operational evaluation of face recognition systems.
  arXiv  Detail & Related papers  (2024-09-03T14:19:38Z)
• Towards Understanding the Impact of Code Modifications on Software Quality Metrics [1.2277343096128712]
  This study aims to assess and interpret the impact of code modifications on software quality metrics. The underlying hypothesis posits that code modifications inducing similar changes in software quality metrics can be grouped into distinct clusters. The results reveal distinct clusters of code modifications, each accompanied by a concise description, revealing their collective impact on software quality metrics.
  arXiv  Detail & Related papers  (2024-04-05T08:41:18Z)
• Free Open Source Communities Sustainability: Does It Make a Difference in Software Quality? [2.981092370528753]
  This study aims to empirically explore how the different aspects of sustainability impact software quality. 16 sustainability metrics across four categories were sampled and applied to a set of 217 OSS projects.
  arXiv  Detail & Related papers  (2024-02-10T09:37:44Z)
• BLEURT Has Universal Translations: An Analysis of Automatic Metrics by Minimum Risk Training [64.37683359609308]
  In this study, we analyze various mainstream and cutting-edge automatic metrics from the perspective of their guidance for training machine translation systems. We find that certain metrics exhibit robustness defects, such as the presence of universal adversarial translations in BLEURT and BARTScore. In-depth analysis suggests two main causes of these robustness deficits: distribution biases in the training datasets, and the tendency of the metric paradigm.
  arXiv  Detail & Related papers  (2023-07-06T16:59:30Z)
• Quantifying Process Quality: The Role of Effective Organizational Learning in Software Evolution [0.0]
  Real-world software applications must constantly evolve to remain relevant. Traditional methods of software quality control involve software quality models and continuous code inspection tools. However, there is a strong correlation and causation between the quality of the development process and the resulting software product.
  arXiv  Detail & Related papers  (2023-05-29T12:57:14Z)
• Regressing Relative Fine-Grained Change for Sub-Groups in Unreliable Heterogeneous Data Through Deep Multi-Task Metric Learning [0.5999777817331317]
  We investigate how techniques in multi-task metric learning can be applied for theregression of fine-grained change in real data. The techniques investigated are specifically tailored for handling heterogeneous data sources.
  arXiv  Detail & Related papers  (2022-08-11T12:57:11Z)
• Deep Relational Metric Learning [84.95793654872399]
  This paper presents a deep relational metric learning framework for image clustering and retrieval. We learn an ensemble of features that characterizes an image from different aspects to model both interclass and intraclass distributions. Experiments on the widely-used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate that our framework improves existing deep metric learning methods and achieves very competitive results.
  arXiv  Detail & Related papers  (2021-08-23T09:31:18Z)
• The Mind Is a Powerful Place: How Showing Code Comprehensibility Metrics Influences Code Understanding [10.644832702859484]
  We investigate whether a displayed metric value for source code comprehensibility anchors developers in their subjective rating of source code comprehensibility. We found that the displayed value of a comprehensibility metric has a significant and large anchoring effect on a developer's code comprehensibility rating.
  arXiv  Detail & Related papers  (2020-12-16T14:27:45Z)
• GO FIGURE: A Meta Evaluation of Factuality in Summarization [131.1087461486504]
  We introduce GO FIGURE, a meta-evaluation framework for evaluating factuality evaluation metrics. Our benchmark analysis on ten factuality metrics reveals that our framework provides a robust and efficient evaluation. It also reveals that while QA metrics generally improve over standard metrics that measure factuality across domains, performance is highly dependent on the way in which questions are generated.
  arXiv  Detail & Related papers  (2020-10-24T08:30:20Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Precise Tradeoffs in Adversarial Training for Linear Regression [55.764306209771405]
  We provide a precise and comprehensive understanding of the role of adversarial training in the context of linear regression with Gaussian features. We precisely characterize the standard/robust accuracy and the corresponding tradeoff achieved by a contemporary mini-max adversarial training approach. Our theory for adversarial training algorithms also facilitates the rigorous study of how a variety of factors (size and quality of training data, model overparametrization etc.) affect the tradeoff between these two competing accuracies.
  arXiv  Detail & Related papers  (2020-02-24T19:01:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.