Innovation of Software Engineering Graduate Designbased on Life Cycle of Software Development

2012 ◽  
pp. 593-597
Author(s):  
Dandan Li ◽  
Liqin Hu ◽  
Xuemei Liu
Keyword(s):  
Life Cycle ◽  
Software Engineering ◽  
Software Development

TRENDS IN LIFE-CYCLE MODELS FOR SE AND KE: PROPOSAL FOR A SPIRAL-CONICAL LIFE-CYCLE APPROACH

1995 ◽  
Vol 05 (03) ◽  
pp. 445-465 ◽  
Author(s):  
F. ALONSO ◽  
N. JURISTO ◽  
J. PAZOS
Keyword(s):  
Life Cycle ◽  
Software Engineering ◽  
Software Development ◽  
21St Century ◽  
Knowledge Engineering ◽  
Life Cycle Approach ◽  
Life Cycle Model ◽  
Cycle Model ◽  
Knowledge Based ◽  
Technological Tools

The ten-year gap between the emergence of Software Engineering (SE) and Knowledge Engineering (KE) has led to the two disciplines developing along different methodological lines. In this paper, we point out that, after having passed through a period during which they ignored each other, followed by a competitive phase, the two disciplines have now reached a meeting point. We see the need for a life-cycle model for systems that integrate traditional and knowledge-based software. Besides, software development in the 21st century will entail open requirements and technological tools that will evolve during the life-cycle. Finally, the paper discusses a proposal for a conical-type spiral life-cycle model that seeks to meet all those needs.

scholarly journals A Security-Specific Knowledge Modelling Approach, Software Practices, and Data Centre Infrastructure for Securing Software Engineering Technologies

2021 ◽  
pp. 324-342
Author(s):  
Abdul Joseph Fofanah ◽  
Habibu Rasin Bundu ◽  
Jonathan Gibrill Kargbo ◽  
Ahmed Fofana
Keyword(s):  
Life Cycle ◽  
Software Engineering ◽  
Software Development ◽  
Process Model ◽  
Life Cycle Model ◽  
Cycle Model ◽  
Specific Knowledge ◽  
Data Centre ◽  
Project Model ◽  
Modelling Approach

The advancement of emerging technological tools in software engineering is an important element in the design and development of software systems. In this paper, we present an analysis of theory and practice including methodology of software products for both large and complex requirements and development analysis, and synthesis. The paper is presented in two folds: Part-I describes a security-specific knowledge of modelling approach for securing software engineering and typical projects implemented in data centre infrastructure. In relation to software engineering practice and theory, we analysed the key parameters indicators of software development projects and the elements of a system that encapsulate the customer, developer, and the researcher as stakeholders in a software development project, whereas the elements of a system entail computer, data validation, mailroom, and computation with paychecks and pay-information. The modelling process and life cycle model includes some major processes in software development such as users’ resources, production of the final product, subprocesses with hierarchy links, process activity, guiding principles, and outcomes of a software requirement specification. In Part-II, an overview of data centre infrastructure and with some schematic illustration for each phase of the construction and implementation of a data centre. The project involves a system and process that creates it with prepare, design, acquire, and implement as a process model whereas actors create the project model. In the context of data centre life cycle model, prepare and design form the construct or build phase, and maintainability and optimization form the engineering phase. All these formulates the project model as the building blocks of data centre. The business need for the construction of the data centre (prepare, design, acquire, and implement) are the knowledge-based of the process model phases to produce an overall system we called the four phases of data centre project process.

scholarly journals Machine Learning: A Software Process Reengineering in Software Development Organization

2019 ◽  
Vol 9 (2) ◽  
pp. 4492-4500
Keyword(s):  
Machine Learning ◽  
Life Cycle ◽  
Software Engineering ◽  
Software Development ◽  
Software Project ◽  
Software Application ◽  
Development Organization ◽  
Organizational Mechanism ◽  
Organizational Ability ◽  
Automation Technology

BPR (Business Process Re-engineering) is an organizational mechanism that improves the organizational ability in responding to the challenges of qualitative result by change management and improvement in software engineering processes, productivity, product quality and competitive advantage. BPR inherits, explores and implements the building of process change, to incorporate enhancements to the essential considerations and protocols of (SEM) Software Engineering Management. Machine Learning (ML) can be the key aspect for BPR in software development organization. The goal of this research study is raising the conceptual vision about integration of automation technology like ML and its life cycle development within Software Development Life Cycle (SDLC) of the software product and highlights benefits and drawbacks ML techniques in SPM (Software Project Management), and how to implement ML in standard SEM practices. We have attempted the introduction of machine learning in SEM to determine specific performance and tasks reuse using empirical analysis and discussion on implementation of ML algorithms. The empirical study of software technologies includes control structure of an autonomous software application. In current era, ML imparts consistently promising accuracy in some SEM fields. The goal of this paper is an empirical and analytical study and literature review to propose desired level of quality software, through the comparative evaluation of existing processes and their respective support for Software Quality Engineering (SQE).

DPSSEE

2011 ◽  
pp. 409-438 ◽  
Author(s):  
Donghua Deng ◽  
Phillip C.Y. Sheu
Keyword(s):  
Life Cycle ◽  
Software Engineering ◽  
Software Development ◽  
Control Design ◽  
Consistency Checking ◽  
Software Life Cycle ◽  
Description Language ◽  
Model Based Testing ◽  
Engineering Environment ◽  
Design Consistency

This chapter presents a distributed proactive semantic software engineering environment (DPSSEE) that incorporates logic rules into a software development process to capture the semantics from all levels of the software life cycle. It introduces the syntax and semantics of the Semantic Description Language (SDL). It also introduces model-based testing and maintenance. Several working scenarios are discussed to illustrate the use of semantic rules for workflow control, design consistency checking, testing, and maintenance.

Classical Methodologies, Techniques and Tools for Project Management

2011 ◽  
pp. 72-94
Author(s):  
Fabrizio Fioravanti
Keyword(s):  
Risk Management ◽  
Life Cycle ◽  
Project Management ◽  
Software Engineering ◽  
Software Development ◽  
Life Cycles ◽  
Software Management ◽  
The Past ◽  
Agile Methodologies

In order to better understand Agile methodologies, it is necessary to have a clear background of what software engineering has suggested in the past regarding the methodologies for approaching software development and software management (Agresti, 1986; Buxton, 1976; Ghezzi, 1990; Naur, 1969). For these reasons, in this chapter, the so-called classical methodologies for project management are considered and commented on, together with the techniques, meta-models such as the spiral life cycle, and tools such as risk management and assessment. It is important to know the background of software engineering in order to understand if Agile methodologies will fit your needs. In this chapter, the waterfall life cycle and a couple of evolutionary life cycles (Gilb, 1988), such as prototyping and spiral life cycles (Boehm, 1988), will be analyzed.

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