20 papers · ranked by Valyu relevance
Lei Qiao, Yan Wang, Jie Zhang
Effort-aware just-in-time (JIT) defect prediction is to rank source code changes based on the likelihood of detects as well as the effort to inspect such changes. Accurate defect prediction algorithms help to find more defects with limited effort. To improve the accuracy of defect prediction, in this paper, we propose…
P. Kumudha, R. Venkatesan
Effective prediction of software modules, those that are prone to defects, will enable software developers to achieve efficient allocation of resources and to concentrate on quality assurance activities. The process of software development life cycle basically includes design, analysis, implementation, testing, and…
Zhen Li, Tong Li, YuMei Wu, Liu Yang + 2 more
In order to improve software quality and testing efficiency, this paper implements the prediction of software defects based on deep learning. According to the respective advantages and disadvantages of the particle swarm algorithm and the wolf swarm algorithm, the two algorithms are mixed to realize the complementary…
Qi Fei, Guisheng Yin, Zhian Sun, Xiangjie Kong
Software defect detection is a critical research topic in the field of software engineering, aiming to identify potential defects during the development process to improve software quality and reduce maintenance costs. This study proposes a novel feature selection and defect prediction classification algorithm based on…
Mikel Robredo, Matteo Esposito, Fabio Palomba, Rafael Peñaloza + 1 more
—Background. Defect prediction has been a highly active topic among researchers in the Empirical Software Engineering field. Previous literature has successfully achieved the most accurate prediction of an incoming fault and identifying the features and anomalies that precede it through just-in-time prediction. As…
Jonathan Bryan, Pablo Moriano, Orawit Thinnukool
The increasing complexity of today’s software requires the contribution of thousands of developers. This complex collaboration structure makes developers more likely to introduce defect-prone changes that lead to software faults. Determining when these defect-prone changes are introduced has proven challenging, and…
Barakat J. Akinsanya, Luiz Jonatã Pires de Araújo, Mariia Charikova, Susanna Gimaeva + 5 more
'Susanna Gimaeva' 'Alexandr Grichshenko' 'Adil Khan' 'Manuel Mazzara' 'Ozioma Okonicha N' 'Daniil Shilintsev'] Abstract. Machine Learning (ML) has become a ubiquitous tool for predicting and classifying data and has found application in several problem domains, including Software Development (SD). This paper reviews…
Manzura Jorayeva, Akhan Akbulut, Cagatay Catal, Alok Mishra + 4 more
Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been…
Fuqun Huang, Lorenzo Strigini
— As the primary cause of software defects, human error is the key to understanding, and perhaps to predicting and avoiding them. Little research has been done to predict defects on the basis of the cognitive errors that cause them. This paper proposes an approach to predicting software defects, so that they may be…
Wei Fu, Tim Menzies, Di Chen, Amritanshu Agrawal
For example, Deb's principle of ϵ-dominance states that if there exists some ϵ value below which it is useless or impossible to distinguish results, then it is superfluous to explore anything less than ϵ. We say that for "large ϵ problems", the results space of learning effectively contains just a few regions. If many…
Muhammad Dhiauddin, Suhaimi Ibrahim
This research describes the initial effort of building a prediction model for defects in system testing carried out by an independent testing team. The motivation to have such defect prediction model is to serve as early quality indicator of the software entering system testing and assist the testing team to manage and…
Sana Gul, Rizwan Bin Faiz, Mohammad Aljaidi, Ghassan Samara + 2 more
Cross-project defect prediction (CPDP) is a significant way of defect identification in the project. In cross-project defect prediction, we extract knowledge from the source project and apply that learned knowledge to predict labels for the target project. However, the model performance can be affected by features that…
Safa Omri, Carsten Sinz
—Over the last years, machine learning techniques have been applied to more and more application domains, including software engineering and, especially, software quality assurance. Important application domains have been, e.g., software defect prediction or test case selection and prioritization. The ability to…
Mrutyunjaya Panda
Software are becoming an indigenous part of human life with the rapid development of software engineering, demands the software to be most reliable. The reliability check can be done by efficient software testing methods using historical software prediction data for development of a quality software system. Machine…
Michael Toriyama, Jiaxing Qu, Lidia C. Gomes, Elif Ertekin
Phase stability, defect formation energies, and carrier concentrations are closely interrelated features of semiconductors. Due to their joint dependence on the multidimensional chemical potential space, it is challenging to quantitatively establish patterns between these quantities in a given semiconductor, especially…
Matthew Witman, Anuj Goyal, Tadashi Ogitsu, Anthony McDaniel + 1 more
We present a graph neural network modeling approach that fully automates the prediction of the DFT-relaxed vacancy formation enthalpy of any crystallographic site from its DFT-relaxed host structure. Applicable to arbitrary structures with an accuracy limited principally by the amount/ diversity of the data on which it…
Authors not listed
Many physical properties of functional materials are governed by their impurities rather than their bulk characteristics. Defects in crystals can activate electronic and ionic conductivity, create active centres for catalysis, or store information through localised spin configurations. Accurate modelling of defect…
Berk Gurdamar, Osman Ugur Sezerman
Classification of a mutation is important for variant prioritization and diagnostics. However, it is still a challenging task that many mutations are classified as variant of unknown significance. Therefore, in silico tools are required for classifying variants with unknown significance. Over the past decades, several…
Staffan Arvidsson McShane, Ulf Norinder, Jonathan Alvarsson, Ernst Ahlberg + 2 more
Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputs of machine learning models and producing valid prediction intervals. We here present the open source software CPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign…
Authors not listed
This study presents a validation and refinement of the “yellow cards” error detection workflow that can be applied to any property connected to molecular structure. In our implementation the workflow employed 5 predictive models with each assigning a “yellow card” to 5% of the entries with worst prediction accuracy.…