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Currently, Extreme Programming, Scrum, and Kanban are the three most commonly used methods in agile software development (ASD) projects. Each method has different practices and shares a set of agile principles, where quality, time, and cost are the three project performance indicators. Companies may focus on and prioritize certain indicators based on industry or project differences. Therefore, choosing appropriate practices that fit the specific performance indicator is an important decision for organizations. This study utilizes a hierarchical consensus model to examine the correlation between four agile practice groups, six agile principle categories, and three project performance indicators. The modified Delphi method was applied to collect the pairwise comparison data, and the analytic hierarchy process was utilized to analyze the data. A Delphi panel of experts from both academia and industry was established to reach a consensus on the correlation priority using pairwise comparison matrices. The principle of cooperation between customer and developer is considered the most important principle related to project time and cost performance, while the technical excellence principle is the most important principle related to project quality performance. These results can assist organizations and practitioners in adopting the ASD practices that will best enhance their competitive advantage.

As a solution to the limitations of traditional in-car navigation systems, this study proposes a new architecture that integrates cloud technology and data science technology. DSV2C2V (Data Science Vehicle-to-Cloud-to-Vehicle) is able to perform multi-dimensional data analysis, including information inside and outside the vehicle. The proposed architecture is expected to help reduce automotive energy consumption and traffic congestion problems.

Many online platforms providing crowd with opportunities to participate in software development projects have been existed for a while. Meanwhile, many enterprises are using crowd source to collaboratively develop their software via these platforms in recent years. However, some software development projects in these platforms hardly attract users to join. Therefore, these project owners need a way to effectively predict the number of participants in their projects and accordingly well plan their software and project specifications, such as the program language and the size of the documentation, in order to attract more individuals to participant in the projects. Compared with the past prediction models, our proposed model can dynamically add the factors, such as number of participants in the initial stage of the project, within the project life cycle and make the adjustment to the prediction model. The proposed model was also verified by using cross validation method. The results show that: 1) The models with the factor “the number of user participation” is more accurate than the model without it. 2) The factors of crowd dimension are more influential on the prediction accuracy than those of software project and owner dimensions. It is suggested that the project owners not only just consider those factors of the software project dimension in the initial stage of the project life cycle but also those factors of crowd and interaction dimensions in the late stage to attract more participants in their projects.

There is currently no development process standard for big data projects. With the increasing number of such projects, the authors designed a new software engineering lifecycle process for big data projects, primarily based on ISO/IEC standard 15288:2008.

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.

The ability to accurately and consistently estimate software development efforts is required by the project managers in planning and conducting software development activities. Since software effort drivers are vague and uncertain, software effort estimates, especially in the early stages of the development life cycle, are prone to a certain degree of estimation errors. A software effort estimation model which adopts a fuzzy inference method provides a solution to fit the uncertain and vague properties of software effort drivers. The present paper proposes a fuzzy neural network (FNN) approach for embedding artificial neural network into fuzzy inference processes in order to derive the software effort estimates. Artificial neural network is utilized to determine the significant fuzzy rules in fuzzy inference processes. We demonstrated our approach by using the 63 historical project data in the well-known COCOMO model. Empirical results showed that applying FNN for software effort estimates resulted in slightly smaller mean magnitude of relative error (MMRE) and probability of a project having a relative error of less than or equal to 0.25 (Pred(0.25)) as compared with the results obtained by just using artificial neural network and the original model. The proposed model can also provide objective fuzzy effort estimation rule sets by adopting the learning mechanism of the artificial neural network.

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.

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.

It is difficult to measure the maintainability of a software system early in the development life cycle from its requirement descriptions written in a natural language because informal specifications cannot be analyzed. With the uses of formal description techniques (FDTs) in the communication protocol area since the mid-1980s, avenues have been opened up for a system to be analyzed early in the specification phase. Quantitative measures on its maintainability can then be extracted from such a formal specification, so that we can develop easily maintainable communication software systems and further reduce the increasingly high cost of software maintenance. To date, there is hardly any work done on measuring the maintainability of a system early in its specification phase. This paper presents a method for measuring the maintainability of a communication by using maintainability metrics derived from its formal specification written in Estelle. The methodology for building the Estelle maintainability metrics hierarchy is presented. We have also developed an automated tool, called PSAMS, to automate the calculation of the maintainability indices. We also found that there is a significant correlation between the specification metrics proposed and the widely adopted implementation metrics, thus demonstrating that our proposed metrics are a reliable means of measuring the maintainability of a communication protocol early in the specification phase.

Good project management is key when developing a software system successfully. To manage a project well, it is important to have the optimal resource allocation which is affected by the size of an implementation. Early software size estimation is essential for good project management. Existing software size models estimate the size of an implementation usually in terms of the number of lines of code. The main drawback of these models is that there is a wide margin of uncertainty as the actual size depends on the type of application and the software development method adopted. To address this drawback, we focus our work on communication protocol, and propose that the size of a formal specification needs to be estimated from an informal specification. This paper presents a two-stage size model for estimating the sizes of a formal communication protocol specification and its implementation, with the model validated using a test data set. The main benefit of this work is that it can give an indication of the likely sizes of both a formal specification and its implementation early at the development stage, giving developers a technique for managing communication software project better.

An obstacle to the uses of software metrics and size models, which we have developed for measuring the complexity and maintainability of a communication protocol specified in Estelle and for estimating the size of its specification and implementation, is the time-consuming effort in collecting the metrics. To address this problem, a software system called PSAMS (protocol specification assessment and measurement system) for automatically calculating the metrics and sizes of specification and implementation has been developed. This paper describes the design of PSAMS, which provides five functionalities for a communication protocol Estelle specification: exploring its specification, measuring its complexity, assessing its maintainability, estimating its specification size and estimating its implementation size. To demonstrate the usefulness of PSAMS, we have applied it to measure the complexity and maintainability of 10 communication protocol Estelle specifications; the measurement results and decision support information provided by each functionality are presented in this paper. With PSAMS, communication protocol designers and developers are able to assess the complexity of a communication protocol early in the specification stage and have information which helps them manage a communication software project better.

Existing software size models estimate the size of an implementation of a software system usually in terms of the number of lines of code. The main drawback of these models is that there is a wide margin of uncertainty, as the actual size depends on the type of application and the software development method adopted. To address this drawback, the authors focus their work on the formal communication protocol development, and present a size model for estimating the size of an Estelle specification of a communication protocol from its informal specification.