Different Phases Of Software Development Lifecycle Research Articles

Framework for the Automation of SDLC Phases using Artificial Intelligence and Machine Learning Techniques

Software Engineering acts as a foundation stone for any software that is being built. It provides a common road-map for construction of software from any domain. Not following a well-defined Software Development Model have led to the failure of many software projects in the past. Agile is the Software Development Life Cycle (SDLC) Model that is widely used in practice in the IT industries to develop software on various technologies such as Big Data, Machine Learning, Artificial Intelligence, Deep learning. The focus on Software Engineering side in the recent years has been on trying to automate the various phases of SDLC namely- Requirements Analysis, Design, Coding, Testing and Operations and Maintenance. Incorporating latest trending technologies such as Machine Learning and Artificial Intelligence into various phases of SDLC, could facilitate for better execution of each of these phases. This in turn helps to cut-down costs, save time, improve the efficiency and reduce the manual effort required for each of these phases. The aim of this paper is to present a framework for the application of various Artificial Intelligence and Machine Learning techniques in the different phases of SDLC.

Swarm intelligence-based model for improving prediction performance of low-expectation teams in educational software engineering projects.

Software engineering is one of the most significant areas, which extensively used in educational and industrial fields. Software engineering education plays an essential role in keeping students up to date with software technologies, products, and processes that are commonly applied in the software industry. The software development project is one of the most important parts of the software engineering course, because it covers the practical side of the course. This type of project helps strengthening students’ skills to collaborate in a team spirit to work on software projects. Software project involves the composition of software product and process parts. Software product part represents software deliverables at each phase of Software Development Life Cycle (SDLC) while software process part captures team activities and behaviors during SDLC. The low-expectation teams face challenges during different stages of software project. Consequently, predicting performance of such teams is one of the most important tasks for learning process in software engineering education. The early prediction of performance for low-expectation teams would help instructors to address difficulties and challenges related to such teams at earliest possible phases of software project to avoid project failure. Several studies attempted to early predict the performance for low-expectation teams at different phases of SDLC. This study introduces swarm intelligence -based model which essentially aims to improve the prediction performance for low-expectation teams at earliest possible phases of SDLC by implementing Particle Swarm Optimization-K Nearest Neighbours (PSO-KNN), and it attempts to reduce the number of selected software product and process features to reach higher accuracy with identifying less than 40 relevant features. Experiments were conducted on the Software Engineering Team Assessment and Prediction (SETAP) project dataset. The proposed model was compared with the related studies and the state-of-the-art Machine Learning (ML) classifiers: Sequential Minimal Optimization (SMO), Simple Linear Regression (SLR), Naïve Bayes (NB), Multilayer Perceptron (MLP), standard KNN, and J48. The proposed model provides superior results compared to the traditional ML classifiers and state-of-the-art studies in the investigated phases of software product and process development.

Birds of a Feather Gel Together: Impact of Team Homogeneity on Software Quality and Team Productivity

Even though a lot of projects fail due to social issues or personality conflicts, only a small number of empirical studies have been conducted to quantitatively assess the impact of individual personality attributes on the software being developed and the team developing that software. The goal of this paper is to quantify the abstract notion of team homogeneity and to measure its impact on software quality and team productivity. A metric called team homogeneity index (THI) is proposed for this purpose. The six-step process of calculating the THI of a software development team is described and illustrated with the help of an example. Finally, the utility of THI is assessed by conducting a controlled experiment in two different phases of the software development life cycle (SDLC), i.e., implementation and testing. The results reveal that, during the implementation phase, teams with greater THI values were noticeably more productive and produced better quality code. Similarly, during the testing phase, teams with higher values of THI tested more features and wrote better quality test cases. Therefore, the evidence obtained so far suggests that the newly proposed metric, THI, appears to be useful in predicting the quality of software and the productivity of software development teams. Future work includes determining the weights of the five traits using input from the software industry and replication of this empirical study on different phases of SDLC with software practitioners to validate our findings.

A study of SDLC to develop well engineered software

Software Development Life Cycle (SDLC) is an important concept used in software engineering to describe a procedure for planning, creating, coding, testing and implementation of user requirement specification. Software development life cycle applies to a range of hardware and software configurations. SDLC is step by step process for creating quality software for users. It involves different phases that are followed one after one, that are essential for software engineers such as planning, analysis, design, coding, testing and implementation. In the early years, hardware was costly and software relatively cheap. In digital era, hardware is cheap and software is expensive. So the costs of hardware and software have been reversed due to increased demand of well engineered software products. This paper includes different phases of SDLC, software quality, qualities of well engineered software and factors affecting software quality. Key words: SDLC, Phases, Software Quality, Factors