Blockchain is a decentralized transaction and data management technology developed first for Bitcoin cryptocurrency. The interest in Blockchain technology has been increasing since the idea was coined in 2008. The reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. In this research, we have conducted a systematic mapping study with the goal of collecting all relevant research on Blockchain technology. Our objective is to understand the current research topics, challenges and future directions regarding Blockchain technology from the technical perspective. We have extracted 41 primary papers from scientific databases. The results show that focus in over 80% of the papers is on Bitcoin system and less than 20% deals with other Blockchain applications including e.g. smart contracts and licensing. The majority of research is focusing on revealing and improving limitations of Blockchain from privacy and security perspectives, but many of the proposed solutions lack concrete evaluation on their effectiveness. Many other Blockchain scalability related challenges including throughput and latency have been left unstudied. On the basis of this study, recommendations on future research directions are provided for researchers.

Software process improvement aims at improving the development process of software systems. It is initiated by process assessment identifying strengths and weaknesses and based on the findings, improvement plans are developed. In general, a process reference model (e.g., CMMI) is used throughout the process of software process improvement as the base. CMMI defines a set of process areas involved in software development and what to be carried out in process areas in terms of goals and practices. Process areas and their elements (goals and practices) are often correlated due to the iterative nature of software development process. However, in the current practice, correlations of process elements are often overlooked in the development of an improvement plan, which diminishes the efficiency of the plan. This is mainly attributed to significant efforts and the lack of required expertise. In this paper, we present a process correlation analysis model that helps identify correlations of process elements from the results of process assessment. This model is defined based on CMMI and empirical data of improvement practices. We evaluate the model using industrial data.

Software process improvement (SPI) has been widely practiced in various domains. SPI uses a process reference model (e.g., CMMI) for planning improvement actions by identifying weaknesses and strengths of the current exercise. Identified findings are analyzed for their relationships to increase the synergy of improvement actions. However, the current practice is monotonic focusing on the identification of weaknesses and strengths. In this work, we present a CMMIbased recommendation method for analyzing correlations of assessment findings. In the model, we define a process correlation model capturing relationships of practices in CMMI. The model is then used for inferring relationships of given findings where findings are viewed as instances of CMMI practices. We take into account both direct and indirect relationships and analyze the precision and recall of the correlation model by different levels of relationship depth. We evaluate the method using industrial data and the results show the potential of the method.

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Software process improvement (SPI) begins with process assessment based on a process reference model such as CMMI. Process improvement action items in SPI are determined according to the identified strengths and weaknesses of the current practice. Therefore, given that a list of assessment findings has been identified, it is important to analyze correlations of findings and identify relevant findings for building improvement items. However, correlation analysis requires expertise and considerable efforts, which makes it difficult for practitioners to perform it in process improvement projects. In this work, we present a CMMI‐based method for identifying correlations of findings and building improvement packages using graph clustering techniques. We evaluate the method using industrial data. Copyright © 2015 John Wiley & Sons, Ltd.