Knowledge Management in Software Process Improvement

This chapter discusses knowledge management (KM) aspects of how software process and software process improvement (SPI) is practiced within very small entities (VSEs) in the context of Irish software development industry. In particular, this study is concerned with the process of software development knowledge management in supporting the SPI. In order to understand the support process, the authors of this chapter have studied how KM processes are practiced within VSEs which includes communication, learning, sharing, and documentation process. This study also focuses in detail on the issues of the knowledge atrophy problem in VSEs. The findings explain how KM has been practiced and influenced the software development process and process improvement in VSEs. This result indicates that KM processes in VSEs are being undertaken in a very informal manner and also in indirect way. This is due to a small team size, autonomous working and macro-management style and caused VSEs to be more informal in their KM processes specifically and SPI generally. In addition, the results have indicated that the informal environment and culture helped VSEs to easily create and share knowledge between staff members and also assisted VSEs to mitigate the knowledge atrophy problem in their organization.

Software Metrics and Design Quality in Object Oriented Paradigm

As the object oriented programming languages and development methodologies moved forward, a significant research effort was spent in defining specific approaches and building models for quality based on object oriented measurements. Software metrics research and practice have helped in building an empirical basis for software engineering. Software developers require objectives and valid measurement schemes for the evaluation and improvisation of product quality from the initial stages of development. Measuring the structural design properties of a software system such as coupling, inheritance, cohesion, and complexity is a promising approach which can lead to an early quality assessment. The class codes and class diagrams are the key artifacts in the development of object oriented (OO) software and it constitutes the backbone of OO development. It also provides a solid foundation for the design and development of software with a greater influence over the system that is implemented. This chapter presents a survey of existing relevant works on class code / class diagram metrics in an elaborate way. Here, a critical review of the existing work is carried out in order to identify the lessons learnt regarding the way these studies are performed and reported. This work facilitates the development of an empirical body of knowledge. The classical approaches based on statistics alone do not provide managers and developers with a decision support scheme for risk assessment and cost reduction. One of the future challenges is to use software metrics in a way that they creatively address and handle the key objectives of risk assessment and the estimation of external quality factors of the software.

Software Reusability Estimation Model Using Metrics Governing Design Architecture

The current trend of open source development and outsourcing industry heavily banks upon the reusability of software for achieving consistency in quality and optimization of cost. Hence, software developers require excellent support in the assessment of the reusability levels of the software that they are trying to develop. This chapter introduces a method which will analyze the object oriented software design and calculate the possible reusability level of that software module. It measures the structural properties of software using design metrics and transforms those measures to reusability levels associated with that package. This scheme brings out a novel data driven-methodology, which can predict the reusability of a software system at its design phase itself and provide a feedback to the design architect to improve the design appropriately to achieve better reusable and cost effective software products.

Towards Understanding the Use of Patterns in Software Engineering

There are a number of avenues of articulating experiential knowledge, including patterns. However, the mere availability of patterns does not lead to their suitable use, if at all. In order to establish a systematic approach for using patterns, a pattern stakeholder model and a general, process environment-neutral and domain-independent pattern usage model are proposed, and the relationships between them are underscored. The underlying essential and accidental concerns in putting patterns into practice by pattern stakeholders are highlighted and, in some cases, possible resolutions are suggested. In particular, challenges in acquisition, selection, and application of patterns are discussed.

Managing Requirements Elicitation Knowledge using a Spatial Hypertext Wiki

Wikis have been widely used as knowledge management tools. However, most of them do not support the conversion process of knowledge in an appropriate way. Specifically, they do not support brainstorming and creativity techniques, which are needed to convert tacit knowledge into explicit. This chapter presents how a wiki tool called the Spatial Hypertext Wiki (ShyWiki) can be used for supporting collaborative requirements elicitation following the knowledge creation spiral of Nonaka. The knowledge conversions in the spiral (socialization, externalization, combination, and internalization) and the knowledge types in each conversion are related to different activities in requirements elicitation, which can be performed through ShyWiki. ShyWiki allows stakeholders to collaborate by creating, brainstorming, structuring and reorganizing requirements contained in notes. In this way, the requirements negotiation and prioritization process can be done through the wiki pages which are seen as virtual boards that hold hypertext notes.

Natural Language Processing Techniques in Requirements Engineering

Requirement analysis is the very first and crucial step in the software development processes. Stating the requirements in a clear manner, not only eases the following steps in the process, but also reduces the number of potential errors. In this chapter, techniques for the improvement of the requirements expressed in the natural language are revisited. These techniques try to check the requirement quality attributes via lexical and syntactic analysis methods sometimes with generic, and sometimes domain and application specific knowledge bases.

Knowledge Engineering Support for Intelligent Software Test Optimization

Software testing is one of the most important processes in Software Development Life Cycle (SDLC) to ensure quality of software product. A large number of approaches for test data generation and optimization in software testing have been reported with varying degrees of success. In recent years, the application of artificial intelligence (AI) techniques in software testing to achieve quality software is an emerging area of research. It combines the concepts of the two domains AI and Software Engineering and yields innovative mechanisms that have the advantages of both of them. This chapter demonstrates the development of a novel software test sequence optimization framework using intelligent agents based graph searching technique. To ensure the quality, a set of test adequacy criteria such as path coverage, state coverage, and branch coverage have been applied in the approach discussed in this chapter, while achieving optimization.

Implications of Markup on the Description of Software Patterns

The reliance on past experience is crucial to the future of software engineering. There are a number of avenues for articulating experiential knowledge, including patterns. It is the responsibility of a pattern author to ensure that the pattern is expressed in a manner that satisfies the reason for its existence. This chapter is concerned with the suitability of a pattern description for consumption by both humans and machines. For that, a pattern description model (PDM), and a pattern stakeholder model (PSM) and a pattern quality model (PQM) as necessary input to the PDM, are proposed. The relationships between these conceptual models are highlighted. The PDM advocates the use of descriptive markup for representation and suggests the use of presentation markup for presentation of information in pattern descriptions, respectively. The separation of representation of information in a pattern description from its presentation is emphasized. The potential uses of the Semantic Web and the Social Web as mediums for publishing patterns are discussed.

Knowledge Management in E-Commerce

Knowledge management refers to acquisition, creation, dissemination, and utilization of knowledge. Knowledge is becoming an important resource for today’s organisations, and enterprises are keen to deploy this resource to improve their products, services, and processes as well as ensure delivery on demand. Through knowledge management, businesses aim to increase efficiency, reduce costs, and most importantly, retain customers. Enterprises in the electronic commerce arena are already aware of the importance of understanding customer attitudes and their buying habits and are, therefore, taking huge strides to ensure that they employ appropriate techniques to manage the information at hand. This chapter discusses the variety of approaches that business enterprises have adopted for the acquisition of customer information and its deployment and outlines the processes relevant to knowledge management in the context of electronic commerce.

Software Process Model using Dynamic Bayesian Networks

This chapter describes a methodology to support the management of large scale software projects in optimizing product correction effort versus initial development costs over time. The Software Process Model (SPM) represents the implementation of this approach on a level of detail explicitly developed to meet project manager’s demands. The underlying technique used in this approach is based on Dynamic Bayesian Networks (DBNs). The use of Bayesian Networks (BNs) enables the representation of causal relationships among process and product key performance indicators elicited either by data or expert knowledge. DBNs provide an insight into various aspects of SPM over time to assess current as well as predicting future model states. The objective of this chapter is to describe the practical approach to establish SPM as state of the art decision support in an industrial environment.