The software quality is one of the most important aspect of software. The aim of this work is to compare chosen software quality models. Several point of view on software quality have been discussed. Software quality models have been presented and compared according to specified criteria.
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The progress which had occurred at the field of computer science caused that small information systems are more often joint in the bigger ones. The aim of this work was to systematize the methods of integration used in management information systems. The methods of integration were divided into three levels: data, functions and processes, exchange of data between the systems. The first part of the work is devoted to the integration at the level of the data realized with use of data warehouses. In the second part the integration at the level of functions (MRP II/ERP/CIM) and processes (workflow) was characterized. The influence of Internet-based technologies on the opportunities of integration was also discussed (SCM, CRM, PRM). The third part raises the problems of exchange of data between the systems (EDI, OBI, XML).
The aim of this paper is to investigate if it is possible to build accurate Bayesian net models for software development effort and quality prediction under two assumptions for model generation: (1) no expert knowledge is incorporated, (2) only small local qualitative data is used. Models generated in this study provide predictions with medium level of accuracy, yet still keeping the literature average. Thus, they can be used to make only rough estimations at the early software development stage. However, they can be a useful base for detailed models incorporating expert knowledge and tailored for individual needs.
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In this paper, we analyze the extended ISBSG dataset, which contains data on a wide range of software projects developed in various companies worldwide. The main aim of this paper is to identify important factors that influence software quality and to investigate the nature of these relationships. This analysis involves using various statistical techniques, both analytical and graphical. We provide a rating for each variable to express the strength of its relationship with software quality. Unlike earlier analyses, we focus on the business perspective and its relationships on software quality. Obtained results may be used do support decision making in software projects, specifically by demonstrating the impact of selected software development practices.
This paper reports on a part of a project aimed at building an probabilistic model for integrated software quality simulation and prediction. This paper discusses results of the questionnaire survey focused on gathering expert knowledge about the factors influencing various features of software quality. Specifically, this analysis identifies project and process factors of software quality, investigates relationships between quality features and their sub-features as well as priorities for quality features. The survey has been performed among software engineering experts and projects managers. Obtained results will be used to calibrate that model for software quality simulation and prediction. These results also partially deliver a general overview on how software quality features are perceived by industry.
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A set of reliable empirical data is often required for a scientific research. For many years in the software engineering domain such datasets were usually not easily publicly available. However, recently some repositories have been established and opened for public access. This paper focuses on the analysis of databases of software projects. I have analyzed such databases grouped in four major repositories: ISBSG, PROMISE, NASA and the databases based on Bugzilla. The contents are diverse among the databases: different parameters describing projects, different level of data granularity and different number of observations. Because of that there is no single database which could be used in each type of research analysis. Rather the aim of the research and the need for specific type of data determines which database could be used. A couple of possible types of analysis can be supported by these datasets: estimation of software size, effort and defects. A few of them allow the trade-off analysis between these factors. Some also contain data about the process and people quality. The comparison of the databases may be a useful tip when the choice of the database needs to be made.
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The aim of this work is to compare selected Bayesian Nets for software project risk assessment. Previous studies have shown that Bayesian Nets have several advantages over other methods of creating models in the domain of software engineering. Thus, I did not analyze other models in this work. The results of this analysis have shown that direct comparison is difficult because of big differences between the models caused by different motivations for building them. Many of those models reflect only small part of software engineering. All of them suffer common weaknesses which are the motivations for my future work focused on creating models overcoming those weaknesses.
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