Article
Computer Science, Artificial Intelligence
Syed Rashid Aziz, Tamim Ahmed Khan, Aamer Nadeem
Summary: The study aims to validate the helpfulness of inheritance metrics in classifying unlabeled datasets and propose a new mechanism to label clusters as faulty or fault-free. Results showed a significant impact of inheritance metrics in SFP, specifically in classifying unlabeled datasets and correctly labeling instances.
PEERJ COMPUTER SCIENCE
(2021)
Article
Chemistry, Multidisciplinary
Elif Nur Haner Kirgil, Tulin Ercelebi Ayyildiz
Summary: The cohesion value is an important factor for evaluating software maintainability. However, measuring cohesion manually can be challenging. This study utilized static code analysis tools and machine learning techniques to predict cohesion values for different software metrics. The results showed that KNN algorithm performed best for LCOM2 and TCC metrics, while the REPTree algorithm was the best for LCC and LSCC metrics. RF, REPTree, and KNN techniques had similar performances and can be used for software cohesion metric prediction.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Syed Rashid Aziz, Tamim Ahmed Khan, Aamer Nadeem
Summary: This study evaluated the exclusive use and viability of inheritance metrics in software fault prediction through experiments with about 40 datasets containing inheritance metrics, finding that ic, noc, and dit metrics are helpful in reducing error entropy rate.
PEERJ COMPUTER SCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Morteza Zakeri-Nasrabadi, Saeed Parsa
Summary: Unlike most other software quality attributes, testability cannot be evaluated solely based on the characteristics of the source code. This paper offers a new equation to estimate testability regarding the size and coverage of a given test suite, and regression models are used to predict testability based on source code metrics.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Khoa Phung, Emmanuel Ogunshile, Mehmet Aydin
Summary: Identifying software faults is crucial in software development as they reduce software quality and increase development costs. Previous studies have provided insufficient information for fault prediction, making the task difficult. This paper proposes a new set of software metrics called Error-type software metrics, which incorporate information about different types of Java runtime errors. The authors also propose a methodology using Stream X-Machine and machine learning techniques to model, extract, and evaluate error patterns. Experimental results demonstrate that the proposed metrics significantly improve fault-proneness prediction performance.
Article
Computer Science, Information Systems
Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Martina Iammarino, Debora Montano
Summary: Technical debt is detrimental to software development, and there is still a need for further research and understanding. This study evaluates the use of quality metrics for accurate prediction of technical debt, providing a useful approach for practical purposes.
FRONTIERS OF COMPUTER SCIENCE
(2023)
Article
Computer Science, Information Systems
Jingxiu Yao, Martin Shepperd
Summary: Software engineering researchers conducted experiments on software defect prediction algorithms, finding that the widely used F1 metric is problematic. By using the MCC metric, a significant proportion of results changed direction, highlighting the importance of using unbiased metrics and publishing detailed results for alternative analyses.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Article
Physics, Multidisciplinary
Ilias Kalouptsoglou, Miltiadis Siavvas, Dionysios Kehagias, Alexandros Chatzigeorgiou, Apostolos Ampatzoglou
Summary: Software security is vital for software development organizations to provide high-quality and reliable software. Early detection of vulnerabilities is crucial, and text mining-based vulnerability prediction models have shown better performance compared to software metrics-based models.
Article
Quantum Science & Technology
Krishna Palem, Duc Hung Pham, M. V. Panduranga Rao
Summary: This paper presents a series of new results on learning concentrated Boolean functions in the quantum computing model. The use of quantum approaches improves the query complexity, but still faces challenges in the case of exact learning without error.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Computer Science, Software Engineering
Dimitrios Tsoukalas, Nikolaos Mittas, Alexander Chatzigeorgiou, Dionysios Kehagias, Apostolos Ampatzoglou, Theodoros Amanatidis, Lefteris Angelis
Summary: Technical Debt (TD) is an effective metaphor for conveying the consequences of software inefficiencies and their elimination to both technical and non-technical stakeholders. To accurately identify and quantify TD, a range of metrics related to source code, repository activity, issue tracking, refactorings, duplication, and commenting rates are used as features for statistical and Machine Learning models. The results show that it is feasible to assess TD in Java projects with sufficient accuracy and reasonable effort, leading to the implementation of an automated TD assessment tool prototype.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Automation & Control Systems
Nikola Konstantinov, Christoph H. Lampert
Summary: This work investigates fairness-aware learning under worst-case data manipulations and reveals that in certain situations, the learner can be forced to return an overly biased classifier, particularly when dealing with learning problems that have underrepresented protected groups in the data. Additionally, the study demonstrates that two learning algorithms that optimize for both accuracy and fairness achieve order-optimality in terms of corruption ratio and protected groups frequencies in the large data limit.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Computer Science, Software Engineering
Francesco Lomio, Sergio Moreschini, Valentina Lenarduzzi
Summary: This study empirically investigates the impact of code quality on fault proneness and predicts fault-inducing commits using different variables and techniques. The results identify a set of features that can accurately predict faults, with deep learning models outperforming machine learning models in terms of accuracy.
EMPIRICAL SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Sushant Kumar Pandey, Anil Kumar Tripathi
Summary: The quality of defect datasets is crucial in software defect prediction, and the Class Imbalance (CI) problem is a significant challenge. Training defect prediction models using noisy and imbalanced data can lead to inconsistent and unsatisfactory results. This study investigates the impact of noise and CI on five baseline SDP models and suggests a model with the highest noise tolerance.
Article
Computer Science, Software Engineering
Stefano Dalla Palma, Dario Di Nucci, Fabio Palomba, Damian A. Tamburri
Summary: This paper explores the role of machine learning in predicting defective infrastructure-as-code (IaC) scripts. The study finds that Random Forest is the best-performing model and that product metrics are more accurate in identifying defective scripts compared to process metrics.
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2022)
Article
Environmental Sciences
Martin Gauch, Frederik Kratzert, Oren Gilon, Hoshin Gupta, Juliane Mai, Grey Nearing, Bryan Tolson, Sepp Hochreiter, Daniel Klotz
Summary: Building accurate rainfall-runoff models is crucial in hydrological science and practice. In this study, expert opinions were compared with quantitative metrics, and it was found that experts generally agreed with the metrics and showed a preference for Machine Learning models over traditional hydrological models. Although there were inconsistencies in expert opinions, where there was agreement, the opinions could be predicted from the quantitative metrics.
WATER RESOURCES RESEARCH
(2023)
Article
Computer Science, Software Engineering
Steffen Herbold, Alexander Trautsch, Jens Grabowski
EMPIRICAL SOFTWARE ENGINEERING
(2017)
Article
Computer Science, Software Engineering
Steffen Herbold, Patrick Harms, Jens Grabowski
INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER
(2017)
Article
Computer Science, Software Engineering
Steffen Herbold, Alexander Trautsch, Jens Grabowski
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2018)
Article
Computer Science, Software Engineering
Steffen Herbold
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2017)
Editorial Material
Computer Science, Software Engineering
Steffen Herbold, Andreas Hoffmann
INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER
(2017)
Correction
Computer Science, Software Engineering
Steffen Herbold, Alexander Trautsch, Jens Grabowski
IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
(2019)
Article
Computer Science, Software Engineering
Steffen Herbold, Alexander Trautsch, Fabian Trautsch, Benjamin Ledel
Summary: The SZZ algorithm, a widely used method for labeling bug fixing commits and finding inducing changes, has been found to have potential problems in accurately determining bug fixing commits. Using a larger set of features in defect prediction data sets does not significantly improve results.
EMPIRICAL SOFTWARE ENGINEERING
(2022)
Article
Computer Science, Software Engineering
Fabian Trautsch, Steffen Herbold, Philip Makedonski, Jens Grabowski
EMPIRICAL SOFTWARE ENGINEERING
(2018)
Proceedings Paper
Computer Science, Hardware & Architecture
Xiaowei Wang, Fabian Glaser, Steffen Herbold, Jens Grabowski
2017 IEEE 10TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD)
(2017)
Proceedings Paper
Computer Science, Software Engineering
Harald Altinger, Steffen Herbold, Friederike Schneemann, Jens Grabowski, Franz Wotawa
2017 IEEE 24TH INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION, AND REENGINEERING (SANER)
(2017)
Proceedings Paper
Computer Science, Information Systems
Fabian Trautsch, Steffen Herbold, Philip Makedonski, Jens Grabowski
13TH WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES (MSR 2016)
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Verena Honsel, Steffen Herbold, Jens Grabowski
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2016, PT III
(2016)
Proceedings Paper
Computer Science, Software Engineering
Steffen Herbold
2015 30th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW)
(2015)
Proceedings Paper
Computer Science, Software Engineering
Steffen Herbold, Alberto De Francesco, Jens Grabowski, Patrick Harms, Lom M. Hillah, Fabrice Kordon, Ariele-Paolo Maesano, Libero Maesano, Claudia Di Napoli, Fabio De Rosa, Martin A. Schneider, Nicola Tonellotto, Marc-Florian Wendland, Pierre-Henri Wuillemin
2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST)
(2015)
Proceedings Paper
Computer Science, Software Engineering
Verena Honsel, Daniel Honsel, Steffen Herbold, Jens Grabowski, Stephan Waack
2015 30TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING WORKSHOP (ASEW)
(2015)