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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.

With the emergence of environmental sustainability and green business management, increasing demands have been made on businesses in the areas of environmental corporate social responsibility (ECSR). Furthermore, the influence of ECSR on green capital investment, environmental performance, and business competitiveness has also been the subject of attention from enterprises. However, in previous studies, the mediating role of green information technology (IT) capital in the relationship between ECSR, environmental performance, and business competitiveness, has not been investigated by researchers. In order to bridge this gap in the ECSR literature, this study aims to examine the influence of ECSR on green IT capital, and the consequent effect of green IT capital on environmental performance and business competitiveness. Data were collected from 358 companies from the top 1000 manufacturers in Taiwan. The results confirmed that ECSR has significant positive effects on green IT human capital, green IT structural capital, and green IT relational capital. Green IT structural capital and green IT relational capital have positive effects on environmental performance and business competitiveness, and environmental performance has a positive effect on business competitiveness. In addition, green IT structural capital and green IT relational capital have partial mediating effects on ECSR, environmental performance, and business competitiveness. The implications and suggestions for future research are discussed.

Despite the fact that the association between business greening and its competitiveness has been confirmed, the effects of green IT capital on the relationship between business greening and competitiveness have largely not been investigated by researchers. To address this gap in the research, this study aims to introduce and define the new concept of green IT capital to bridge the gap for business greening. The results of a sample survey of 148 companies from the top 1,000 manufacturers in Taiwan confirmed that the degree of business greening has significant positive effects on business investment in green IT human capital, green IT structural capital, and green IT relational capital as well as business competitiveness. In addition, the three dimensions of green IT capital positively relate to business competitiveness. In mediating effect, green IT relational capital partially mediates the links between the degree of business greening and business competitiveness.

Aligning information technology (IT) and business objectives is an important task for enterprises. IT service management (ITSM) is one of the methods used to achieve this mission. ITSM implementation often involves huge investment with unexpected risks. Understanding which are the critical areas and how to manage these can help in not only reducing the risks but can also provide helpful guidance for enterprises. However, relatively few studies exist on the critical success factors (CSFs) related to ITSM implementation. This paper presents the results of a modified Delphi study of ITSM professionals on the relative importance of various CSFs for implementing ITSM. A total of 13 factors are identified by the Pareto principle, and the rationales behind the factors are investigated. Finally, the implications based on the findings are given for the management of ITSM implementation.

Housing purchase at different stages of life cycle of various people is a complicated but important decision. Classification rule mining is a common technology in the field of data mining. Classification Rules can be generalized to classify unknown samples or predict the future from the mining of historical data. This paper proposes a rule-based approach to housing purchase life cycle on demands through C4.5 classification method. An example, a real estate transaction data set in a government, is utilized in the paper to illustrate the utility of the proposed approach and further evaluated its prediction accuracy, precision and recall. The established prediction model with the aid of the derived classification rules helps various people make appropriate decisions following different housing purchase life cycle stages on demands.

Recently, information technology service management (ITSM) has emerged as a challenging research area. ITSM implementation often involves huge investment with unexpected risks. Therefore, understanding the preparedness of critical success factors (CSFs) and its effect on the organizational performance can help in not only reducing the risks but in also providing helpful guidance for enterprises. This empirical study explores the preparedness of ITSM-relevant CSFs and its effect on the organizational performance. The findings show that (1) the more important a CSF is, the more prepared the CSF is; (2) the organizational performance is mostly beneficial from customer and internal business process perspectives; and (3) only a few CSFs can significantly affect the organizational performance. Finally, the implications based on the findings are given for the management of ITSM implementation.

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.

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.

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.

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.

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.