Software Reliability Prediction using Correlation Constrained Multi-Objective Evolutionary Optimization Algorithm

Software reliability frameworks are extremely effective for estimating the probability of software failure over time. Numerous approaches for predicting software dependability were presented, but neither of those has shown to be effective. Predicting the number of software faults throughout the research and testing phases is a serious problem. As there are several software metrics such as object-oriented design metrics, public and private attributes, methods, previous bug metrics, and software change metrics. Many researchers have identified and performed predictions of software reliability on these metrics. But none of them contributed to identifying relations among these metrics and exploring the most optimal metrics. Therefore, this paper proposed a correlation- constrained multi-objective evolutionary optimization algorithm (CCMOEO) for software reliability prediction. CCMOEO is an effective optimization approach for estimating the variables of popular growth models which consists of reliability. To obtain the highest classification effectiveness, the suggested CCMOEO approach overcomes modeling uncertainties by integrating various metrics with multiple objective functions. The hypothesized models were formulated using evaluation results on five distinct datasets in this research. The prediction was evaluated on seven different machine learning algorithms i.e., linear support vector machine (LSVM), radial support vector machine (RSVM), decision tree, random forest, gradient boosting, k-nearest neighbor, and linear regression. The result analysis shows that random forest achieved better performance

Product vs. Design Quality of Object-Oriented Software

Abstract: Assessment of quality of object oriented software during design phase has been a prime objective among researchers in software engineering discipline. There are many available tools that offer the software product quality from its code. Unfortunately, assessment of design quality has been more of a theoretical issue and the software engineering market lacks readily available tools for this purpose. We make use of a set of UML diagrams created during the design phase of the development process to calculate design quality. UMLet software has been used for creating the UML diagrams. Design metrics are fetched from the UML diagrams using a parser developed by us and design quality is assessed using a hierarchical model of software design quality. To validate the design quality, we compute the product quality from software code that corresponds to the UML design diagrams using available tools METRIC 1.3.6, JHAWK and Team In a Box. The objective is to establish a correspondence between design quality and product quality of object oriented software and thus identifying the design metrics that play decisive role in the quality of a software product. For this purpose, we have chosen seven software of known quality as low, medium and high. A strong correlation has been observed among the results obtained from the two approaches i.e., between design quality and the product quality, which validates our chosen metrics set for determining software design quality

Deep Learning Method for Recognition and Classification of Images from Video Recorders in Difficult Weather Conditions

The sustainable functioning of the transport system requires solving the problems of identifying and classifying road users in order to predict the likelihood of accidents and prevent abnormal or emergency situations. The emergence of unmanned vehicles on urban highways significantly increases the risks of such events. To improve road safety, intelligent transport systems, embedded computer vision systems, video surveillance systems, and photo radar systems are used. The main problem is the recognition and classification of objects and critical events in difficult weather conditions. For example, water drops, snow, dust, and dirt on camera lenses make images less accurate in object identification, license plate recognition, vehicle trajectory detection, etc. Part of the image is overlapped, distorted, or blurred. The article proposes a way to improve the accuracy of object identification by using the Canny operator to exclude the damaged areas of the image from consideration by capturing the clear parts of objects and ignoring the blurry ones. Only those parts of the image where this operator has detected the boundaries of the objects are subjected to further processing. To classify images by the remaining whole parts, we propose using a combined approach that includes the histogram-oriented gradient (HOG) method, a bag-of-visual-words (BoVW), and a back propagation neural network (BPNN). For the binary classification of the images of the damaged objects, this method showed a significant advantage over the classical method of convolutional neural networks (CNNs) (79 and 65% accuracies, respectively). The article also presents the results of a multiclass classification of the recognition objects on the basis of the damaged images, with an accuracy spread of 71 to 86%