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Quantum software engineering is advancing in the domain of quantum computing research and application, yet the documentation is scattered. The slow transition from Von-Neumann based computation systems to quantum systems, and conserving the fundamental computing principles in software development and software engineering helps in enrichment of quantum software development. The evolution of quantum computing over the past years shows a shift in the domain of classical computation to quantum computation in the years to come. Future applications such as, quantum AI and quantum machine learning will benefit from quantum software engineering. This survey collects and explores the various documentations in the domain of quantum systems and quantum software engineering. The survey provides an in-depth exploration of quantum programming languages, which is combined with explanations of quantum computing's fundamentals. The review also goes in-depth about quantum software engineering and quantum software life cycle development, outlining the quantum software reuse methodology that is introduced in the quantum software lifecycle development domain. [ABSTRACT FROM AUTHOR]

In software engineering, the planning and budgeting stages of a software project are of great importance to all stakeholders, including project managers as well as clients. The estimated costs and scheduling time needed to develop any software project before and/or during startup form the basis of a project's success. The main objective of soft- ware estimation techniques is to determine the actual effort and/or time required for project development. The use of machine learning methods to address the estimation problem has, in general, proven remarkably successful for many engineering problems. In this study, a fully connected neural network (FCNN) model and a metaheuristic, gray wolf optimizer (GWO), called GWO-FC, is proposed to tackle the software development effort estimation (SEE) problem. The GWO is integrated with FCNN to optimize the FCNN parameters in order to enhance the accuracy of the obtained results by improving the FCNN's ability to explore the parameter search field and avoid falling into local optima. The proposed technique was evaluated utilizing various benchmark SEE datasets. Furthermore, various recent algorithms from the literature were employed to verify the GWO-FC performance. In terms of accuracy, comparative outcomes reveal that the GWO-FC performs better than other methods in most datasets and evaluation criteria. Experimental outcomes reveal the strong potential of the GWO-FC method to achieve reliable estimation results. [ABSTRACT FROM AUTHOR]

Requirement Engineering has radicalized data analytics by playing a pivotal role in planning requirements strategies and activities. It has emerged as a leading domain of software engineering that branches into 2 categories namely functional requirements (FRs) and non-functional requirements (NFRs). Functional Requirements have gained eminence and prominence while NFRs have always faced a step-sisterly treatment. It is a lit large in the literature review that NFRs are very crucial and never considered appropriately and consequently, the systems have failed. The present study presents an automated system for efficient NFRs prediction. The proposed system consists of 4 layers for the prediction of NFRs significance including Data acquisition layer (DA), feature selection and extraction layer (FSE), data extraction and mining layer (DEM), and data analysis and visualization layer (DAV). Moreover, the current research considers the probability measure of the level of NFR significance in terms of LoNFRS, which is cumulatively quantified as NFRs significance measure (NFRINDEX). NFRINDEX has been quantified for prediction purposes using an convolution neural network (CNN). Additionally, the existence of NFRs is visualized based on a self organized mapping (SOM) procedure. To validate the proposed system, a primary dataset is collected from several IT professionals and academicians. The NFRs dataset of 312 IT professionals and academicians has been gathered for 17 attributes resulting in 5304 instances. Numerous experimental simulations were performed to assess performance in terms of classification, reliability, stability, feature selection, and prediction efficiency. [ABSTRACT FROM AUTHOR]

Raising the design abstraction level has been, since many years, a prominent solution to decrease the complexity of self-adaptive real time embedded systems (RTES) and increase productivity. However, the manual specification and integration of the adaptive behavior into the system model is still presenting a big handicap for designers. There is a lack of Computer Aided Software Engineering tools and methodologies that permit to automate the generation of self-adaptive RTES models. Therefore, designers need an expertise in the adaptation domain as well as the modeling language and much time for manual work. At the aim of solving these problems, we propose, in this paper, a Model Driven Engineering based approach that permits to guide the automatic transformation of a classic Y-chart RTES model into a self-adaptive model based on design patterns. A model-based interactive process is developed to enable the generation of self-adaptive models for RTES based on the instantiation, integration and specialization of design patterns. The generated system model is then mapped to the input of a simulation platform in order to perform the non-functional properties verification and simulate the system reconfiguration, if needed. The proposed approach is tested and validated through a case study of an embedded video conferencing system which is CPU and network bandwidth-constrained. [ABSTRACT FROM AUTHOR]

The term risk is defined as the potential future harm that may arise due to some present actions. Risk management in software engineering is related to the various future harms that could be possible on the software due to some minor or non-noticeable mistakes in software development project or process. There are quite different types of risk analysis that can be used. Basically, risk analysis identifies the high risk elements of a project in software engineering. Also, it provides ways of detailing the impact of risk mitigation strategies. Risk analysis has also been found to be most important in the software design phase to evaluate criticality of the system. The main purpose of risk analysis understands the risks in better ways and to verify and correct the attributes. A successful risk analysis includes important elements like problem definition, problem formulation, data collection. Some of the requirement risks are Poor definition of requirements, Inadequate of requirements, Lack of testing, poor definition of requirements etc. The likelihood of the events which tends to the goal can be evaluated from the evidence of Satisfaction and denial of the goal and it can be achieved through Tropos goal model. Original Tropos model is modified to meet the risk assessment requirements in requirements engineering. The event considers as a risk which based on the likelihood values. The relations are defined between multiple goals and events, which identify the necessity of a particular goal. In order to analyze the risk in achieving some particular goals, a set of candidate solutions are generated. Based on the risk affinitive value, the candidate solutions can be evaluated. There are three risk parameters to compute the risk affinitive value, which are (1) low (2) medium (3) high. The risk parameters and cost analysis clearly evaluate the affinity of that event to a particular set of goals. [ABSTRACT FROM AUTHOR]

Hybrid genetic algorithms (HGAs) have recognized significant attention in recent years and being progressively more used to solve real-world problems. HGA is the forms of assimilation between genetic algorithm and other search optimization techniques to improve the overall performance of the genetic algorithm (GA). GA can be hybridized with many other bio-inspired heuristic algorithms such as particle swarm optimization, cuckoo search genetic algorithm, firefly algorithm-genetic algorithm, real coded genetic algorithm-artificial fish swarm algorithm, etc., multi-agent system (MAS) is a new paradigm for conceptualizing, designing and optimizing the solution models. Multi-agent learning concept improves the outcome of the MAS and the type of learning process involved in it plays a vital role. Incremental model is a well-known software development process in which the system yields better effect at the end of every cycle. In this paper, software engineering inspired incremental model based multi-agent learning model for permutation coded genetic algorithms has been proposed. One of the famous combinatorial hard problems of traveling salesman problem (TSP) is being chosen as the test bed and the experiments are performed on large sized benchmark TSP instances obtained from standard TSPLIB. The experimental results support that the proposed model perform better than existing best working initialization methods in terms of convergence rate, error rate and computation time. [ABSTRACT FROM AUTHOR]

Software architecture plays an important role in the field of Software engineering. The main challenge of software design is to improve the quality attributes. Design phase plays an important part during this process. The designers have to take care of the complex situations. Quality of the design must also be considered during the development. The existing software architecture design methods are having the demerits with the lower quality service reliability and others. So a latest and highly developed software design patterns are necessary to work out the above drawback. This article suggests a new methodology to carry out the software design with newest set of design patterns. We proposed an esteemed software patterns for banking enterprises. The proposed work is done by extending the choice of these design patterns with bank managers and analyzed, that design patterns are highly usefulness for the designers in designing the software with esteemed patterns and preeminent quality attributes. [ABSTRACT FROM AUTHOR]

Defects occurring in software product are a universal event. Prevention of these defects in the early stage needs more attention because early stage prevention and fixing requires less effort and lower cost. Software defect prediction (SDP) is necessary in the determination of software quality as well as reliability. Prediction of defects is relatively an original research area in software quality engineering. Coverage of key predictors and the kind of data to be collected along with defect prediction model role, the interdependence of defects and predictors can be recognized in software quality. Feature selection (FS) is one of the worthy preprocessing techniques for application that uses huge volumes of data. It is the process of selecting the probable minimal attribute which is expected to be represented in the set of actual attributes. This paper proposes, FS using firefly algorithm (FA) and classifiers like support vector machine (SVM), Naïve Bayes (NB) as well as K-nearest neighbor (KNN) are used for classifying the features selected. The FS that make use of the FA is that new technique of evolutionary computation that has been inspired by the process of flash lighting of the fireflies. This can search quickly the feature space for an optimal or a near optimal feature subset for minimizing a certain function of fitness. This proposed fitness function has made use of the incorporation of both the accuracy of classification and the reduction of the size. The results of the experiment have shown that the FS using the FA can achieve a better accuracy of classification than that of the other methods. [ABSTRACT FROM AUTHOR]

In growing Software industry, whenever engineers developed new software, they want to make sure that it is failure free and reliable. With the increasing reliability of hardware and growing complexity of software, the software reliability is a rising concern for both developer and users. Software reliability is the key part of quality and customer satisfaction. For the last three decades, many software reliability models have been successfully utilized in practical software reliability engineering. However, no single model can obtain the accurate prediction for all cases. This paper proposed the software reliability model with the increased number of training data set and neural networks. The back propagation algorithm has been chosen and applied for a learning process. The obtained results show that the proposed model increases the accuracy of the software reliability prediction. [ABSTRACT FROM AUTHOR]

In software engineering fault proneness prediction is one of the important fields for quality measurement using multiple code metrics. The metrics thresholds are very practical in measuring the code quality for fault proneness prediction. It helps to improvise the software quality in short time with very low cost. Many researchers are in the race to develop a measuring attribute for the software quality using various methodologies. Currently so many fault proneness prediction models are available. Among that most of the methods are used to identify the faults either by data history or by special supervising algorithms. In most of the real time cases the fault data bases may not be available so that the process becomes tedious. This article proposes a hybrid model for identifying the faults in the software models and also we proposed coupling model along with the algorithm so that the metrics are used to identify the faults and the coupling model couples the metrics and the faults for the developed system software. [ABSTRACT FROM AUTHOR]