In the soft-to-hard technical spectrum: Where is software engineering?

In the computer journals and tabloids, there have been a plethora of articles written about the software engineering field. But while advocates of the need for an engineering approach to software development, it is impressive how many authors have treated the subject of software engineering without adequately addressing the fundamentals of what engineering as a discipline consists of. A discussion is presented of the various related facets of this issue in a logical framework to advance the thesis that the software development process is necessarily an engineering process. The purpose is to examine more of the details of the issue of whether or not the design and development of software for digital computer processing systems should be both viewed and treated as a legitimate field of professional engineering. Also, the type of academic and professional level education programs that would be required to support a software engineering discipline is examined

The Framework For The Discipline Of Software Engineering in Connection to Information Technology Discipline

This paper represents preliminary work in identifying the foundation for the discipline of Software Engineering and discovering the links between the domains of Software Engineering and Information Technology (IT). Our research utilized IEEE Transactions on Software Engineering (IEEE-TSE), ACM Transactions on Software Engineering and Methodology (ACM-TOSEM), Automated Software Engineering (ASE), the International Conference on Software Engineering(ICSE), and other related journal publication in the software engineering domain to address our research questions. We explored existing frameworks and described the need for software engineering as an academic discipline. We went further to clarify the distinction difference between Software Engineering and Computer Science. Through this efforts we contribute to an understanding of how evidence from IT research can be used to improve Software Engineering as a discipline

Designing a consulting services architecture model

textDuring my years of experience in the technology industry, it has become obvious that standard processes and methodologies within the engineering discipline are at a mature state. The realization though is that software engineering specifically lags behind. Most software engineering methodologies that I have studied focus on the mission of software development. It is this realization and the need for structure that led me to review existing methodologies used within my company's software services organization. The definition of what a successful software services methodology entails is rather limited. This report will provide a history of existing software engineering methodologies that I have studied, describe an initial services method that was being developed within my organization, develop a new model that addresses previous shortcomings and identify additional components required to further define a strong software services-oriented delivery methodology.Electrical and Computer Engineerin

Machine Learning for Software Engineering: Models, Methods, and Applications

Machine Learning (ML) is the discipline that studies methods for automatically inferring models from data. Machine learning has been successfully applied in many areas of software engineering ranging from behaviour extraction, to testing, to bug fixing. Many more applications are yet be defined. However, a better understanding of ML methods, their assumptions and guarantees would help software engineers adopt and identify the appropriate methods for their desired applications. We argue that this choice can be guided by the models one seeks to infer. In this technical briefing, we review and reflect on the applications of ML for software engineering organised according to the models they produce and the methods they use. We introduce the principles of ML, give an overview of some key methods, and present examples of areas of software engineering benefiting from ML. We also discuss the open challenges for reaching the full potential of ML for software engineering and how ML can benefit from software engineering methods

In search for a widely applicable and accepted software quality model for software quality engineering

Software Quality Engineering is an emerging discipline that is concerned with improving the approach to software quality. It is important that this discipline be firmly rooted in a quality model satisfying its needs. In order to define the needs of this discipline, the meaning of quality is broadly defined by reviewing the literature on the subject. Software Quality Engineering needs a quality model that is usable throughout the software lifecycle and that it embraces ail the perspectives of quality. The goal of this paper is to propose a quality model suitable for such a purpose, through the comparative evaluation of existing quality models and their respective support for Software Quality Engineering

A Taxonomy for a Constructive Approach to Software Evolution

In many software design and evaluation techniques, either the software evolution problem is not systematically elaborated, or only the impact of evolution is considered. Thus, most of the time software is changed by editing the components of the software system, i.e. breaking down the software system. The software engineering discipline provides many mechanisms that allow evolution without breaking down the system; however, the contexts where these mechanisms are applicable are not taken into account. Furthermore, the software design and evaluation techniques do not support identifying these contexts. In this paper, we provide a taxonomy of software evolution that can be used to identify the context of the evolution problem. The identified contexts are used to retrieve, from the software engineering discipline, the mechanisms, which can evolve the software software without breaking it down. To build such a taxonomy, we build a model for software evolution and use this model to identify the factors that effect the selection of software evolution\ud mechanisms. Our approach is based on solution sets, however; the contents of these sets may vary at different stages of the software life-cycle. To address this problem, we introduce perspectives; that are filters to select relevant elements from a solution set. We apply our taxonomy to a parser tool to show how it coped with problematic evolution problems