Deterioration of software quality developed by multiple organizations has become a serious problem. To predict software degradation after an organizational change, this paper investigates the influence of quality deterioration on software metrics by analyzing three software projects. To detect factors indicating a low evolvability, we focus on the relationships between the change in software metric values and refactoring tendencies. Refactoring after an organization change impacts the quality.

Software defect prediction (SDP) plays a significant part in allocating testing resources reasonably, reducing testing costs, and ensuring software quality. One of the most widely used algorithms of SDP models is Naive Bayes (NB) because of its simplicity, effectiveness and robustness. In NB, when a data set has continuous or numeric attributes, they are generally assumed to follow normal distributions and incorporate the probability density function of normal distribution into their conditional probabilities estimates. However, after conducting a Kolmogorov-Smirnov test, we find that the 21 main software metrics follow non-normal distribution at the 5% significance level. Therefore, this paper proposes an improved NB approach, which estimates the conditional probabilities of NB with kernel density estimation of training data sets, to help improve the prediction accuracy of NB for SDP. To evaluate the proposed method, we carry out experiments on 34 software releases obtained from 10 open source projects provided by PROMISE repository. Four well-known classification algorithms are included for comparison, namely Naive Bayes, Support Vector Machine, Logistic Regression and Random Tree. The obtained results show that this new method is more successful than the four well-known classification algorithms in the most software releases.

In this letter we develop a software reliability modeling framework by introducing the Burr XII distributions to software fault-detection time. An extension to deal with software metrics data characterizing the product size, program complexity or testing expenditure is also proposed. Finally, we investigate the goodness-of-fit performance and compare our new models with the existing ones through real data analyses.

An exponential regression-based model with stochastic intensity is developed to describe the software reliability growth phenomena, where the software testing metrics depend on the intensity process. For such a generalized modeling framework, the common maximum likelihood method cannot be applied any more to the parameter estimation. In this paper, we propose to use the pseudo maximum likelihood method for the parameter estimation and to seek not only the model parameters but also the software reliability measures approximately. It is shown in numerical experiments with real software fault data that the resulting software reliability models based on four parametric approximations provide the better goodness-of-fit performance than the common non-homogeneous Poisson process models without testing metric information.

Software inspection is one of the most effective methods to detect defects. However, inspections are not always worthwhile. This letter proposes an inspection cost model to describe inspections-related costs and extended metrics to evaluate the cost effectiveness of software inspections.

Properly representation of the complexity of class structure will be useful in object oriented software developments. Although some class complexity metrics have been proposed, they have ignored directions of coupling relationships among methods and attributes, such as whether a method writes data onto an attribute or reads data from the attribute. In this paper, we use a directed graph model to represent such coupling relationships. Based on the directed graph model, we propose a metric of class structural complexity. The proposed metric satisfies necessary conditions of complexity metric suggested by Briand and others. The following fact is showed by experimental data of Java classes. While the proposed metric follows a conventional metric, the proposed metric can capture an aspect of class structural complexity which is lost by the conventional one.

A knowledge gap between network operators and system developers in Network Management System (NMS) construction has widened. This has been caused by an expansion of supported business processes and increasingly sophisticated network management functions. This gap makes system development costly and time consuming. Function development, led by operators, is a promising solution to the problems caused by the gap. This type of development should not require an operator to know how to develop NMS. Standard objects may be used to meet this requirement and save time and the cost of NMS construction. However, they are not sufficient to design functions supporting some tasks that are for providing custom services. In this paper, we propose a partial extension package, composed of several object classes. This package is attached to the standard objects to design a custom function. Information processing in a new function can be added, and easily modified, using this package. This package specifies states that invoke the information processing. It also includes objects that add new data without changing standard objects. It makes use of several design patterns in order to weaken coupling to the standard objects. We have applied this package to two programs. One plans maintenance tasks schedules, the other sets threshold values for quality of service. We made use of software metrics to measure their performance in terms of flexibility. The results show that the proposed package continues to make it possible to reuse the standard objects, and makes it easy to modify the behavior of a new function.