A conceptual Bayesian net model for integrated software quality prediction

Software quality can be described by a set of features, such as functionality, reliability, usability, efficiency, maintainability, portability and others. There are various models for software quality prediction developed in the past. Unfortunately, they typically focus on a single quality feature. The main goal of this study is to develop a predictive model that integrates several features of software quality, including relationships between them. This model is an expert-driven Bayesian net, which can be used in diverse analyses and simulations. The paper discusses model structure, behaviour, calibration and enhancement options as well as possible use in fields other than software engineering

Empirical support for the generation of domain-oriented quality models

The difficulties of software quality evaluation found during our activity in different projects and publications led us to investigate a systematic method for building domain-oriented quality models based on a sound empirical basis. General quality models need to be adapted to specific types of software products to be effective. Related literature reveals that existing quality models tend to suffer from poor empirical support. To overcome this situation, a review of applicable existing standards and related literature has enabled the generation of a basis for software quality models which can be adapted to specific application domains. The process, called DUMOD (Domain-oriented qUality MOdels Development), includes the collection of experts' opinions and the application of multivariate analysis techniques in order to eliminate redundant information and construct a validated model, more efficient and reliable. In this study, the empirical method to devise quality models for specific application domains is presented, as well as its application to a case study for security software products empirically validated by extensive collection of data from IT professionals.0.671 JCR (2010) Q3, 73/99 Computer science, software engineeringUE