Change Prediction through Coding Rules Violations

Tollin, Irene; Fontana, Francesca Arcelli; Zanoni, Marco; Roveda, Riccardo

doi:10.1145/3084226.3084282

Cited by 20 publications

(19 citation statements)

References 10 publications

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“…Research on [77][78][79] has more correctly established a new hybrid model. The model is ideal, even for a wider variety of activities, since it is usable in one database.…”

Section: Studies Conducted On Machine Learning Methodsmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

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Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

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“…Research on [77][78][79] has more correctly established a new hybrid model. The model is ideal, even for a wider variety of activities, since it is usable in one database.…”

Section: Studies Conducted On Machine Learning Methodsmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

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“…Considering TD at level code, the estimation has been evaluated from the general point of view of approaches and strategies [10,25,30], and how to measure it, especially considering SonarQube [6,24,27]. Another aspect evaluated is the financial aspect of TD [2].…”

Section: Related Workmentioning

confidence: 99%

“…The largest percentage of TD repayment is created by a small subset of issue types [6], and the most frequently introduced TD items are related to low-level coding issues [24]. Only a few works investigated TD estimation based on SonarQube rules, considering the change-and fault-proneness [12,16,27]. Previous research highlights that developers are not completely sure about the rules' usefulness [28], [26] provided by SonarQube.…”

Section: Related Workmentioning

confidence: 99%

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How long do Junior Developers take to Remove Technical Debt Items?

Lenarduzzi

Mandić

Katin

et al. 2020

Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background. Software engineering is one of the engineering fields with the highest inflow of junior engineers. Tools that utilize source code analysis to provide feedback on internal software quality, i.e. Technical Debt (TD), are valuable to junior developers who can learn and improve their coding skills with minimal consultations with senior colleagues. Objective. We aim at understating which SonarQube TD items junior developers prioritize during the refactoring and how long they take to refactor them. Method. We designed a case study with replicated design and we conducted it with 185 junior developers in two countries, that developed 23 projects with different programming languages and architectures. Results. Junior developers focus homogeneously on different types of TD items. Moreover, they can refactor items in a fraction of the estimated time, never spending more than 50% of the time estimated by SonarQube. Conclusion. Junior Developers appreciate the usage of SonarQube and considered as a useful tool. Companies might ask junior developers to quickly clean their code.

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“…Tollin et al [20] used coding rules violations to predict code changes through a set of machine learning models. Their results indicated that these classification models achieved satisfactory performance, especially when predicting changes in the next version.…”

Section: Change Prediction Modelsmentioning

confidence: 99%

Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

Liu

et al. 2018

2018 25th Asia-Pacific Software Engineering Conference (APSEC)

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Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing studies empirically confirmed that the presence of code smells increases the likelihood of subsequent changes (i.e., changeproness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smellbased metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort.

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Change Prediction through Coding Rules Violations

Cited by 20 publications

References 10 publications

Software Project Management Using Machine Learning Technique—A Review

Software Project Management Using Machine Learning Technique—A Review

How long do Junior Developers take to Remove Technical Debt Items?

Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

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