DBBRBF- Convalesce optimization for software defect prediction problem using hybrid distribution base balance instance selection and radial basis Function classifier

Software is 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 Learning plays a vital role in optimizing the prediction of defect-prone modules in real life software for its effectiveness. The software defect prediction data has class imbalance problem with a low ratio of defective class to non-defective class, urges an efficient machine learning classification technique which otherwise degrades the performance of the classification. To alleviate this problem, this paper introduces a novel hybrid instance-based classification by combining distribution base balance based instance selection and radial basis function neural network classifier model (DBBRBF) to obtain the best prediction in comparison to the existing research. Class imbalanced data sets of NASA, Promise and Softlab were used for the experimental analysis. The experimental results in terms of Accuracy, F-measure, AUC, Recall, Precision, and Balance show the effectiveness of the proposed approach. Finally, Statistical significance tests are carried out to understand the suitability of the proposed model.Comment: 32 pages, 24 Tables, 8 Figures

A paired learner-based approach for concept drift detection and adaptation in software defect prediction

The early and accurate prediction of defects helps in testing software and therefore leads to an overall higher-quality product. Due to drift in software defect data, prediction model performances may degrade over time. Very few earlier works have investigated the significance of concept drift (CD) in software-defect prediction (SDP). Their results have shown that CD is present in software defect data and tha it has a significant impact on the performance of defect prediction. Motivated from this observation, this paper presents a paired learner-based drift detection and adaptation approach in SDP that dynamically adapts the varying concepts by updating one of the learners in pair. For a given defect dataset, a subset of data modules is analyzed at a time by both learners based on their learning experience from the past. A difference in accuracies of the two is used to detect drift in the data. We perform an evaluation of the presented study using defect datasets collected from the SEACraft and PROMISE data repositories. The experimentation results show that the presented approach successfully detects the concept drift points and performs better compared to existing methods, as is evident from the comparative analysis performed using various performance parameters such as number of drift points, ROC-AUC score, accuracy, and statistical analysis using Wilcoxon signed rank test. Keywords: concept drift; naive Bayes; random forest; software defect prediction; software quality assurance.publishedVersio

Degradation Detection in a Redundant Sensor Architecture

Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In taking such an approach, readings from two redundant sensors are continuously checked and compared. As soon as the discrepancy between two redundant lines deviates by a certain threshold, the 1oo2 voter (comparator) assumes that there is a fault in the system and immediately activates the safe state. In this work, we propose a novel fault prognosis algorithm based on the discrepancy signal. We analyzed the discrepancy changes in the 1oo2 sensor configuration caused by degradation processes. Several publicly available databases were checked, and the discrepancy between redundant sensors was analyzed. An initial analysis showed that the discrepancy between sensor values changes (increases or decreases) over time. To detect an increase or decrease in discrepancy data, two trend detection methods are suggested, and the evaluation of their performance is presented. Moreover, several models were trained on the discrepancy data. The models were then compared to determine which of the models can be best used to describe the dynamics of the discrepancy changes. In addition, the best-fitting models were used to predict the future behavior of the discrepancy and to detect if, and when, the discrepancy in sensor readings will reach a critical point. Based on the prediction of the failure date, the customer can schedule the maintenance system accordingly and prevent its entry into the safe state—or being shut down

COMPUTERIZED SOFTWARE QUALITY EVALUATION WITH NOVEL ARTIFICIAL INTELLIGENCE APPROACH

Software quality assurance has grown in importance in the fast-paced world of software development. One of trickiest parts of creating and maintaining software is predicting how well it will perform. The term "computer evaluation" refers to use of advanced AI techniques in software quality assurance, replacing human evaluations and paving the way for a new era in software evaluation. We proposed Hybrid Elephant herding optimized Conditional Long short-term memory (HEHO-CLSTM) to estimate Software Quality Prediction. Software quality prediction and assurance has grown in importance in ever-changing world of software development. Software quality prediction encompasses a wide range of activities aimed at improving the quality of software systems via the use of data-driven approaches for prediction, evaluation and enhancement. We have collected Software Defects data and we feature extracted the attributes using linear discriminant Analysis (LDA). The suggested system improves the accuracy, AUC and Buggy instance compared with the current methods

A Review Of Training Data Selection In Software Defect Prediction

The publicly available dataset poses a challenge in selecting the suitable data to train a defect prediction model to predict defect on other projects. Using a cross-project training dataset without a careful selection will degrade the defect prediction performance. Consequently, training data selection is an essential step to develop a defect prediction model. This paper aims to synthesize the state-of-the-art for training data selection methods published from 2009 to 2019. The existing approaches addressing the training data selection issue fall into three groups, which are nearest neighbour, cluster-based, and evolutionary method. According to the results in the literature, the cluster-based method tends to outperform the nearest neighbour method. On the other hand, the research on evolutionary techniques gives promising results but is still scarce. Therefore, the review concludes that there is still some open area for further investigation in training data selection. We also present research direction within this are

Techniques for calculating software product metrics threshold values: A systematic mapping study

Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation. 2021 by the authors. Licensee MDPI, Basel, Switzerland.Funding: Authors thanks to the Molde University College-Specialized Univ. in Logistics, Norway for the support of Open access fund.Scopus2-s2.0-8512089773

A Quantitative Analysis Between Software Quality Posture and Bug-fixing Commit

Software quality assessment and prediction has been a research hotspot and has become even more critical in continuous software engineering. Modifications to a software product developed following a continuous software engineering process typically commence as a sequence of frequent commits, following a philosophy of “commit small, commit often.” Continuous integration (CI) and continuous deployment (CD) are essential concepts in this development environment. The challenge then is to develop techniques and tools which allow the development team to assess the overall quality posture of a software module in the period from a bug-inducing commit (i.e., when a bug is reported) to a bug-fixing commit (i.e. when a bug is reported fixed. The hypothesis is that in this period, the quality posture of the software modules involved in a bug-inducing/bug-fixing commit pair undergoes changes which may give developers insights that a bug-fixing commit is not only within reach but also the overall quality posture of the system is improving. In this thesis, we perform a quantitative analysis of how the posture of a software module changes and whether those changes follow a pattern that can be used as a predictor for an imminent bug-fixing commit. In this thesis, the posture of a module is denoted by a vector of metrics values computed from the source code and from information extracted from GitHub and Bugzilla repositories. The results indicate that a considerable number of bug-fixing commits in many software projects is preceded by a typical posture, and the occurrences of some posture combinations are more likely than others to be succeeded by a bug-fixing commit

Estimativa do Peso de Corvinas e Deteção de Períodos de Alimentação

O presente trabalho de investigação tem como objetivo explorar a aplicação de modelos de machine learning e deep learning a imagens obtidas em tanques que agregam múltiplos peixes (fish farms). O correto desenvolvimento dos seres vivos presentes nestes tanques envolve processos de controlo minuciosos, não só das condições do meio como também das características dos próprios animais. O peso é uma destas características e o seu controlo fornece informações importantes relativamente ao processo de crescimento e à saúde dos animais. É frequente que os processos de controlo utilizados periodicamente pelas instituições responsáveis pela criação e desenvolvimento de determinados seres vivos sejam realizados de forma manual, o que implica não só um consumo de tempo significativo como também poderá colocar em risco o bem-estar do ser vivo e do individuo responsável. Na tentativa de reduzir a janela temporal necessária para a recolha de dados relativos ao peso de corvinas que habitam as fish farms da empresa SEAentia é proposta a utilização de um procedimento, composto por um modelo YOLOv4, por um script em Python e por um modelo de regressão linear simples, capaz de realizar estimativas de peso para cada ser vivo. Adicionalmente, é proposta também a utilização do mesmo modelo de visão por computador e de um script de pós-processamento para identificação de períodos de alimentação, caracterizados pelo agrupamento das corvinas numa determinada região das fish farms.This research work aims to explore the application of machine learning and deep learning models to images obtained from tanks that aggregate multiple fish (fish farms). The correct development of the living beings present in these tanks involves detailed control processes, not only of the environmental conditions but also of the characteristics of the animals themselves. Weight is one of these characteristics and its control provides important information regarding the growth process and the health of the animals. It is common for monitoring processes used periodically by institutions responsible for the breeding and development of certain living creatures to be done manually, which not only implies a significant consumption of time but may also put at risk the welfare of the living being and the individual responsible. In an attempt to reduce the time window required to collect data on the weight of croakers present in SEAentia's fish farms, it is proposed to use a procedure, composed of a YOLOv4 model, a Python script, and a simple linear regression model, capable of making weight estimates for each living creature. Additionally, it is also proposed to use the same computer vision model and a postprocessing script to identify feeding periods, which are characterized by the existence of groups of meagres in a certain region of the fish farms