Application of Service-Oriented Architecture in Software Quality Management

ACM Computing Classification System (1998): D.2.5, D.2.9, D.2.11.

Data Mining for Software Development Life Cycle Quality Management

Computer software plays an important role in business, government, society and sciences. To solve real-world problems, it is very important to measure the quality and reliability in the software development life cycle (SDLC). Software Engineering (SE) is the computing field concerned with designing, developing, implementing, maintaining and modifying software. The present paper gives an overview of the Data Mining (DM) techniques that can be applied to various types of SE data in order to solve the challenges posed by SE tasks such as programming, bug detection, debugging and maintenance. A specific DM software is discussed, namely one of the analytical tools for analyzing data and summarizing the relationships that have been identified. The paper concludes that the proposed techniques of DM within the domain of SE could be well applied in fields such as Customer Relationship Management (CRM), eCommerce and eGovernment. ACM Computing Classification System (1998): H.2.8.

Quality Assurance in Extreme Programming

Our previous research about possible quality improvements in Extreme Programming (XP) led us to a conclusion that XP supports many good engineering practices but there is still place for refinements. Our proposal was to add dedicated Quality Assurance (QA) measures, which should be sufficiently effective and at the same time simpler enough in the context of XP. This paper intends to analyze the possibilities for an effective way for applying approved quality assurance practices to XP. The last should not affect negatively to the process and in the meantime must lead to better quality assurance. We aim to make changes to XP that even if would slow down a bit the development process, will make it more suitable for widest range of projects including large and very large projects as well as life critical and highly reliable systems.

Cognitive Approach in Castings’ Quality Control

Every year production volume of castings grows, especially grows production volume of non-ferrous metals, thanks to aluminium. As a result, requirements to castings quality also increase. Foundry men from all over the world put all their efforts to manage the problem of casting defects. In this article the authors present an approach based on the use of cognitive models that help to visualize inner cause-and-effect relations leading to casting defects in the foundry process. The cognitive models mentioned comprise a diverse network of factors and their relations, which together thoroughly describe all the details of the foundry process and their influence on the appearance of castings’ defects and other aspects.. Moreover, the article contains an example of a simple die casting model and results of simulation. Implementation of the proposed method will help foundry men reveal the mechanism and the main reasons of casting defects formation.