Quality assessment framework to rank software projects

Alqmase, Mohammed; Alshayeb, Mohammad; Ghouti, Lahouari

doi:10.1007/s10515-022-00342-0

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…People are depending more and more on software systems in their daily lives due to the growth of computer science, software development technology, and digital infor-mation technology, which raise the standards for software quality [1][2][3]. Software system failure results from defects in the system, which puts the security of the system in grave danger [4,5]. Software defect prediction (SDP) technology, however, can make it easier for testers and software system developers to find bugs in software.…”

Section: Introductionmentioning

confidence: 99%

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Javed,

Shengbing,

Asim

et al. 2024

Algorithms

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Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1- measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1- measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance.

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Section: Introductionmentioning

confidence: 99%

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Javed,

Shengbing,

Asim

et al. 2024

Algorithms

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Digital Information Credibility: Towards a Set of Guidelines for Quality Assessment of Grey Literature in Multivocal Literature Review

Abrar

Khan

et al. 2023

Applied Sciences

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Credibility, in general, can be interpreted as a sense of trust in someone. The credibility of information remarkably influences the public’s willingness to do or not to do some things. In this research study, the credibility of digital news stories can be interpreted as the sense of confidence a person has in a source of available information that affects their behavior. Humans spread less credible information instead of more credible information very quickly because humans take an interest in fear, disgust, and surprise. Less credible news may affect individuals as well as economies. Therefore, there is a dire need in the current digital era to find out what affects the credibility of digital news stories. This study aims to review the published literature and the grey literature to determine the factors affecting digital news credibility and the factors that build credibility in digital news stories. In this paper, we have developed a multivocal literature review protocol to assess the credibility of digital news stories. The multivocal literature review is an advanced version of the systematic literature review that searches for grey literature in addition to the published literature. The expected outcomes after implementing our protocol will be a list of credibility factors and their practices that can play a vital role in ensuring the credibility of digital news stories. Based on this protocol, we formulated guidelines for the quality assessment of grey literature. The future direction of this research is to analyze the factors through multi-criteria decision-making (MCDM), i.e., analytical hierarchical process.

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Quality assessment framework to rank software projects

Cited by 2 publications

References 30 publications

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Cross-Project Defect Prediction Based on Domain Adaptation and LSTM Optimization

Digital Information Credibility: Towards a Set of Guidelines for Quality Assessment of Grey Literature in Multivocal Literature Review

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