The software organizations widely consider the cloud based global software development (CGSD) as it offer the quality projects with low cast. The adoption of CGSD is challenging due to the geographical distance between practitioners. This study aims to identify and analyses the motivators that could positively impact the implementation of CGSD paradigm. The systematic literature review approach was applied to identify the CGSD motivators reported in the literature, and were further validated with industry experts using questionnaire survey study. Moreover, the fuzzy-AHP approach was applied to prioritize the investigated motivators concerning their significance for the successful adoption of CGSD. The findings of the study provide the prioritization-based taxonomy of the investigated motivators that assists the software organizations to develop and revise their strategies for the successful implementation of CGSD.

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Multicriteria Decision Making Taxonomy of Cloud-Based Global Software Development Motivators

A challenging task in security engineering concerns the specification and integration of security with other requirements at the top level of requirements engineering Empirical studies show that it is commonly at the business process level that customers and end users are able to express their security needs In addition, systems are often developed by automating existing manual business processes Since many security notions belongs conceptually to the world of business processes, it is natural to try to capture and express them in the context of business models in which moreover customers and end users feel most comfortable In this paper, based on experience drawn from an ongoing work within the CASENET project [1], we propose a UML-based business process-driven framework for the development of security-critical systems

Towards a business process-driven framework for security engineering with the UML

The accelerated growth in exploiting vulnerabilities due to errors or failures in the software development process is a latent concern in the Software Industry. In this sense, this study aims to provide an overview of the Secure Software Development trends to help identify topics that have been extensively studied and those that still need to be. Therefore, in this paper, a systematic mapping review with PICo search strategies was conducted. A total of 867 papers were identified, of which only 528 papers were selected for this review. The main findings correspond to the Software Requirements Security, where the Elicitation and Misuse Cases reported more frequently. In Software Design Security, recurring themes are security in component-based software development, threat model, and security patterns. In the Software Construction Security, the most frequent topics are static code analysis and vulnerability detection. Finally, in Software Testing Security, the most frequent topics are vulnerability scanning and penetration testing. In conclusion, there is a diversity of methodologies, models, and tools with specific objectives in each secure software development stage.

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Systematic Mapping of the Literature on Secure Software Development

Managing the cloud‐based software development outsourcing (CSDO) activities across the geographically distributed development sites are much challenging. This study aims to identify the success factors (SFs) for CSDO and prioritize them based on their significance. To achieve this objective, we conducted a systematic literature review (SLR) and survey study with industrial and academic experts. Finally, we applied the analytical hierarchy process (AHP) to develop the framework based on the prioritization of the identified SFs. We believe that the findings of this study will assist the industry practitioners and researchers in developing effective strategies for the successful implementation of CSDO activities.

A robust framework for cloud‐based software development outsourcing factors using analytical hierarchy process

The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

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A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Context: The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. However, the full potential of machine learning for improving the software engineering life cycle itself is yet to be discovered, i.e., up to what extent machine learning can help reducing the effort/complexity of software engineering and improving the quality of resulting software systems. To date, no comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. Objective: This article addresses the aforementioned problem and aims to present a state-of-the-art on the growing number of uses of machine learning in software engineering. Method: We conduct a systematic mapping study on applications of machine learning to software engineering following the standard guidelines and principles of empirical software engineering. Results: This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages. Overall, 227 articles were rigorously selected and analyzed as a result of this study. Conclusion: From the selected articles, we explore a variety of aspects that should be helpful to academics and practitioners alike in understanding the potential of adopting machine learning techniques during software engineering projects.

Machine Learning for Software Engineering: A Systematic Mapping.

Since the world is moving towards secure systems which makes security a primary concern and not an afterthought in software development. Secure software development involves security at each step of development lifecycle from requirements phase to testing. With surging focus on security requirements, we can see an increase in frameworks/methods/techniques proposed to deal with security requirements for variable applications. However, to summarise the literature findings till date and to propose further ways to handle security requirements a systematic and comprehensive review is needed. Our objective is to conduct a systematic mapping study for cyber-physical systems: (i) to explore and analyse security requirements engineering frameworks/methods/techniques proposed till date, (ii) to investigate on their strengths and weaknesses, and (iii) to determine the security threats and requirements reported in literature. We conducted a systematic mapping study for which we defined our goals and determined research questions, defined inclusion/exclusion criteria, and designed the map systematically based on the research questions. The search yielded 337 articles after deploying the query on multiple databases and refining the search iteratively through a multistep process. The mapping study identified and categorised the existing security requirements engineering frameworks/methods/techniques focused on their implementation and evaluation mechanisms. Second, we identified and categorised the proposed to deal with security requirements for multiple domains, determined their strengths/weaknesses, and also security requirements and threats reports in the selected studies. The study provides an overall view of the state-of-the-art frameworks/methods/techniques proposed till date to deal with security requirements. The results of this study provide insights to researchers to focus more on developing frameworks to deal with security requirements for particular kinds of systems like cyber-physical systems. Also, it motivates future work to devise methods to cater domain specific security risks and requirements.

A Systematic Mapping Study on Security Requirements Engineering Frameworks for Cyber-Physical Systems

Software development industry is growing rapidly and so are the time and budget constraints getting stringent. After Scrum, the widely adopted agile method, agile practitioners are now shifting towards Kanban due to its effective communication facilitation, transparency and limited work in progress traits. Since, the industry is in transition from scrum to Kanban therefore we don't find many empirical studies yielding results of adopting Kanban. Therefore, in this study we aim to explore more on Kanban teams. Mainly, we aim to find the impact of Kanban team's communication patterns on their iteration performance and quality. The findings revealed that the centralization communication patterns have negative impact on iteration performance and quality of a project. However, small world communication pattern has positive impact on iteration performance and quality of a project.

Communication Patterns of Kanban Teams and Their Impact on Iteration Performance and Quality

Communication among agile software development teams is knit around the requirements (user stories) and is considered vital for information sharing. Researchers have studied communication among agile teams from various perspectives including team distribution, distance, and communication patterns etc. It is worth noticing here that most of the advances done in this domain are for Scrum teams. However, minimal work has been done on exploring the communication among Kanban teams. Since, software industry is taking a big leap from Scrum to Kanban and this encourages us to investigate and find interesting insights on Kanban teams' communication. Therefore, in this study we present our roadmap to study communication patterns of Kanban teams. We believe that the findings of the study will contribute to strengthen the understanding on the topic and will also provide a good comparison of communication patterns of scrum and Kanban teams.

Saad Shafiq

Papers

Machine Learning for Software Engineering: A Systematic Mapping.

A Systematic Mapping Study on Security Requirements Engineering Frameworks for Cyber-Physical Systems

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Communication Patterns of Kanban Teams and Their Impact on Iteration Performance and Quality

Towards Studying the Communication Patterns of Kanban Teams: A Research Design