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We explored the relationship between organizational culture and deployment of software process improvement (SPI) approaches using a competing values framework. Our results indicated that the organizational culture had an influence on SPI deployment, primarily made possible by a hierarchic culture with its emphasis on procedures, order, and stability. Clan culture, with its emphasis on human development, commitment to others, and participation, appears to be a necessary condition in creating skills development and sharing SPI knowledge in the process of its deployment. Software Engineering Program Group leaders should ensure that internal values are in place to enhance SPI deployment.

Many problem factors in the software development phase affect the maintainability of the delivered software systems. Therefore, understanding software development problem factors can help in not only reducing the incidence of project failure but can also ensure software maintainability. This study focuses on those software development problem factors which may possibly affect software maintainability. Twenty-five problem factors were classified into five dimensions; a questionnaire was designed and 137 software projects were surveyed. A K-means cluster analysis was performed to classify the projects into three groups of low, medium and high maintainability projects. For projects which had a higher level of severity of problem factors, the influence on software maintainability becomes more obvious. The influence of software process improvement (SPI) on project problems and the associated software maintainability was also examined in this study. Results suggest that SPI can help reduce the level of severity of the documentation quality and process management problems, and is only likely to enhance software maintainability to a medium level. Finally, the top 10 list of higher-severity software development problem factors was identified, and implications were discussed.

Software classification models have been regarded as an essential support tool in performing measurement and analysis processes. Most of the established models are single-cycled in the model usage stage, and thus require the measurement data of all the model's variables to be simultaneously collected and utilized for classifying an unseen case within only a single decision cycle. Conversely, the multi-cycled model allows the measurement data of all the model's variables to be gradually collected and utilized for such a classification within more than one decision cycle, and thus intuitively seems to have better classification efficiency but poorer classification accuracy. Software project managers often have difficulties in choosing an appropriate classification model that is better suited to their specific environments and needs. However, this important topic is not adequately explored in software measurement and analysis literature. By using an industrial software measurement dataset of NASA KC2, this paper explores the quantitative performance comparisons of the classification accuracy and efficiency of the discriminant analysis (DA)- and logistic regression (LR)-based single-cycled models and the decision tree (DT)-based (C4.5 and ECHAID algorithms) multi-cycled models. The experimental results suggest that the re-appraisal cost of the Type I MR, the software failure cost of Type II MR and the data collection cost of software measurements should be considered simultaneously when choosing an appropriate classification model.

Precision in estimating the required software development effort plays a critical factor in the success of software project management. Most existing software effort estimation models only compare the accuracies of software effort estimates from the historical data without clustering. A potential factor that can affect the accuracies of the established effort estimation models is the homogeneity of the data. However, such investigation on the effects of the accuracies of the derived effort estimates is seldom explored in software effort estimation literature. Therefore, this paper aims to explore the effects of accuracies of the software effort estimation models established from the clustered data by using the International Software Benchmarking Standards Group (ISBSG) repository. The ordinary least square (OLS) regression method is adopted to establish a respective effort estimation model in each cluster of datasets. The empirical experiment results show that the estimation accuracies do not reveal significant differences within the respective dataset clustered by each software effort driver. It also demonstrates that software effort estimation models from the clustered data present almost similar accuracy results compared to models from the entire data without clustering.

Accurate estimates of efforts in software development are necessary in project management practices. Project managers or domain experts usually conduct software effort estimation using their experience; hence, subjective or implicit estimates occur frequently. As most software projects have incomplete information and uncertain relations between effort drivers and the required development effort, the grey relational analysis (GRA) method has been applied in building a formal software effort estimation model for this study. The GRA in the grey system theory is a problem-solving method that is used when dealing with similarity measures of complex relations. This paper examines the potentials of the software effort estimation model by integrating a genetic algorithm (GA) to the GRA. The GA method is adopted to find the best fit of weights for each software effort driver in the similarity measures. Experimental results show that the software effort estimation using an integration of the GRA with GA method presents more precise estimates over the results using the case-based reasoning (CBR), classification and regression trees (CART), and artificial neural networks (ANN) methods.

Software projects often fail. Thus it is important to find ways to ensure a successful outcome. One significant area is a better understanding of the relationship between the software project duration and risk exposure, as this helps project managers with pertinent information to be effective in managing risky projects. We addressed this need by adopting a cluster analysis technique to provide managers with insight into effective planning and control of their projects. The results not only revealed that risk exposures associated with user, requirement, planning & control and team risk dimensions were affected by project duration, but also showed how to manage software risks effectively through observing trends in the risk components. Based on our findings, project managers can adopt appropriate attitudes, skills, and practices to deal with risky areas more effectively rather than just identifying those software risks with which project managers should be concerned.

Analogy-based estimation is a widely adopted problem solving method that has been evaluated and confirmed in software effort or cost estimation domains. The similarity measures between pairs of projects play a critical role in the analogy-based software effort estimation models. Such a model calculates a distance between the software project being estimated and each of the historical software projects, and then retrieves the most similar project for generating an effort estimate. Although there exist numerous analogy-based software effort estimation models in literature, little theoretical or experimental works have been reported on the method of deriving an effort estimate from the adjustment of the reused effort based on the similarity distance. The present paper investigates the effect on the improvement of estimation accuracy in analogy-based estimations when the genetic algorithm method is adopted to adjust reused effort based on the similarity distances between pairs of projects. The empirical results show that applying a suitable linear model to adjust the analogy-based estimations is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the proposed model is comparable with those obtained when using other effort estimation methods.

Risk management and performance enhancement have always been the focus of software project management studies. The present paper shows the findings from an empirical study based on 115 software projects on analyzing the probability of occurrence and impact of the six dimensions comprising 27 software risks on project performance. The MANOVA analysis revealed that the probability of occurrence and composite impact have significant differences on six risk dimensions. Moreover, it indicated that no association between the probability of occurrence and composite impact among the six risk dimensions exists and hence, it is a crucial consideration for project managers when deciding the suitable risk management strategy. A pattern analysis of risks across high, medium, and low-performance software projects also showed that (1) the ''requirement'' risk dimension is the primary area among the six risk dimensions regardless of whether the project performance belongs to high, medium, or low; (2) for medium-performance software projects, project managers, aside from giving importance to ''requirement risk'', must also continually monitor and control the ''planning and control'' and the ''project complexity'' risks so that the project performance can be improved; and, (3) improper management of the ''team'', ''requirement'', and ''planning and control'' risks are the primary factors contributing to a low-performance project.

A reliable and accurate estimate of software development effort has always been a challenge for both the software industry and academia. Analogy is a widely adopted problem solving technique that has been evaluated and confirmed in software effort or cost estimation domains. Similarity measures between pairs of effort drivers play a central role in analogy-based estimation models. However, hardly any research has addressed the issue of how to decide on suitable weighted similarity measures for software effort drivers. The present paper investigates the effect on estimation accuracy of the adoption of genetic algorithm (GA) to determine the appropriate weighted similarity measures of effort drivers in analogy-based software effort estimation models. Three weighted analogy methods, namely, the unequally weighted, the linearly weighted and the nonlinearly weighted methods are investigated in the present paper. We illustrate our approaches with data obtained from the International Software Benchmarking Standards Group (ISBSG) repository and the IBM DP services database. The experimental results show that applying GA to determine suitable weighted similarity measures of software effort drivers in analogy-based software effort estimation models is a feasible approach to improving the accuracy of software effort estimates. It also demonstrates that the nonlinearly weighted analogy method presents better estimate accuracy over the results obtained using the other methods.

An essential decision that must be made by software organizations that adopt the continuous representation of the capability maturity model integration (CMMI) for software process improvement concerns a suitable path that best meets their business objectives and mitigates the organization's risk. However, the CMMI models released by the Software Engineering Institute do not give their adopters any guidance on how to make such a decision. Thus, managers often make subjective selections of the areas in which to implement process improvement. Our study presents a decision support model that assists managers in determining the priorities of the CMMI process areas based on the characteristics of the is being developed. The proposed model was validated by using the ISBSG repository, and an example is presented to demonstrate the application of the model. Given the fact that hardly any research has yet been done on how to select the CMMI process areas to initialize process improvement, this study provides a starting point for the community in considering this important issue.

A distance-k dominating set D of a directed graph G is a set of vertices such that for every vertex v of G, there is a vertex u ∈ D and the distance between u and v is at most k. Minimum distance-k dominating set is especially important in communication networks for distributed data structures and for server placement. In this paper, we show that there is a unique mi aimum distance-k dominating set for k = 1,2 in a directed split-star, which has recently been developed as a new model of the interconnection network for parallel and distributed computing systems. Moreover, we shall present simple distributed algorithms for finding such sets.

To help industrialize the use of formal description techniques in the software development of communication protocols, improving the software project management is as equally important as improving the software development techniques. An early software project estimation is a prerequisite for the quantitative software project management to be started early in the development life cycle. However, relatively little work has been done on such an issue in the formal communication protocol development environment. This paper proposes a two-stage software sizing process and product decomposition technique for establishing Estelle specification and implementation size models tailored to the formal communication protocol development environment. Based on the size estimates obtained from the size models above, this paper also presents the practical application in conducting a software cost model COCOMO II to estimate the development cost and project schedule of the ISO ROSE protocol development.