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Inheritance metrics feats in unsupervised learning to classify unlabeled datasets and clusters in fault prediction

ABSTRACT: Fault prediction is a necessity to deliver high-quality software. The absence of training data and mechanism to labeling a cluster faulty or fault-free is a topic of concern in software fault prediction (SFP). Inheritance is an important feature of object-oriented development, and its metrics measure the complexity, depth, and breadth of software. In this paper, we aim to experimentally validate how much inheritance metrics are helpful to classify unlabeled data sets besides conceiving a novel mechanism to label a cluster as faulty or fault-free. We have collected ten public data sets that have inheritance and C&K metrics. Then, these base datasets are further split into two datasets labeled as C&K with inheritance and the C&K dataset for evaluation. K-means clustering is applied, Euclidean formula to compute distances and then label clusters through the average mechanism. Finally, TPR, Recall, Precision, F1 measures, and ROC are computed to measure performance which showed an adequate impact of inheritance metrics in SFP specifically classifying unlabeled datasets and correct classification of instances. The experiment also reveals that the average mechanism is suitable to label clusters in SFP. The quality assurance practitioners can benefit from the utilization of metrics associated with inheritance for labeling datasets and clusters.

SUBMITTER: Aziz S

PROVIDER: S-EPMC8576544 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Software is a complex entity, and its development needs careful planning and a high amount of time and cost. To assess quality of program, software measures are very helpful. Amongst the existing measures, coupling is an important design measure, which computes the degree of interdependence among the entities of a software system. Higher coupling leads to cognitive complexity and thus a higher probability occurrence of faults. Well in time prediction of fault-prone modules assists in saving time and cost of testing. This paper aims to capture important aspects of coupling and then assess the effectiveness of these aspects in determining fault-prone entities in the software system. We propose two coupling metrics, i.e., Vovel-in and Vovel-out, that capture the level of coupling and the volume of information flow. We empirically evaluate the effectiveness of the Vovel metrics in determining the fault-prone classes using five projects, i.e., Eclipse JDT, Equinox framework, Apache Lucene, Mylyn, and Eclipse PDE UI. Model building is done using univariate logistic regression and later Spearman correlation coefficient is computed with the existing coupling metrics to assess the coverage of unique information. Finally, the least correlated metrics are used for building multivariate logistic regression with and without the use of Vovel metrics, to assess the effectiveness of Vovel metrics. The results show the proposed metrics significantly improve the predicting of fault prone classes. Moreover, the proposed metrics cover a significant amount of unique information which is not covered by the existing well-known coupling metrics, i.e., CBO, RFC, Fan-in, and Fan-out. This paper, empirically evaluates the impact of coupling metrics, and more specifically the importance of level and volume of coupling in software fault prediction. The results advocate the prudent addition of proposed metrics due to their unique information coverage and significant predictive ability.

Project description:MotivationProtein phosphorylation is a post-translational modification that underlines various aspects of cellular signaling. A key step to reconstructing signaling networks involves identification of the set of all kinases and their substrates. Experimental characterization of kinase substrates is both expensive and time-consuming. To expedite the discovery of novel substrates, computational approaches based on kinase recognition sequence (motifs) from known substrates, protein structure, interaction and co-localization have been proposed. However, rarely do these methods take into account the dynamic responses of signaling cascades measured from in vivo cellular systems. Given that recent advances in mass spectrometry-based technologies make it possible to quantify phosphorylation on a proteome-wide scale, computational approaches that can integrate static features with dynamic phosphoproteome data would greatly facilitate the prediction of biologically relevant kinase-specific substrates.ResultsHere, we propose a positive-unlabeled ensemble learning approach that integrates dynamic phosphoproteomics data with static kinase recognition motifs to predict novel substrates for kinases of interest. We extended a positive-unlabeled learning technique for an ensemble model, which significantly improves prediction sensitivity on novel substrates of kinases while retaining high specificity. We evaluated the performance of the proposed model using simulation studies and subsequently applied it to predict novel substrates of key kinases relevant to insulin signaling. Our analyses show that static sequence motifs and dynamic phosphoproteomics data are complementary and that the proposed integrated model performs better than methods relying only on static information for accurate prediction of kinase-specific substrates.Availability and implementationExecutable GUI tool, source code and documentation are freely available at https://github.com/PengyiYang/KSP-PUEL.Contactpengyi.yang@nih.gov or jothi@mail.nih.govSupplementary informationSupplementary data are available at Bioinformatics online.

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Inheritance metrics feats in unsupervised learning to classify unlabeled datasets and clusters in fault prediction

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