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Together with many success stories, promises such as the increase in production speed and the improvement in stakeholders' collaboration have contributed to making agile a transformation in the software industry in which many companies want to take part. However, driven either by a natural and expected evolution or by contextual factors that challenge the adoption of agile methods as prescribed by their creator(s), software processes in practice mutate into hybrids over time. Are these still agile In this article, we investigate the question: what makes a software development method agile We present an empirical study grounded in a large-scale international survey that aims to identify software development methods and practices that improve or tame agility. Based on 556 data points, we analyze the perceived degree of agility in the implementation of standard project disciplines and its relation to used development methods and practices. Our findings suggest that only a small number of participants operate their projects in a purely traditional or agile manner (under 15%). That said, most project disciplines and most practices show a clear trend towards increasing degrees of agility. Compared to the methods used to develop software, the selection of practices has a stronger effect on the degree of agility of a given discipline. Finally, there are no methods or practices that explicitly guarantee or prevent agility. We conclude that agility cannot be defined solely at the process level. Additional factors need to be taken into account when trying to implement or improve agility in a software company. Finally, we discuss the field of software process-related research in the light of our findings and present a roadmap for future research.
Context: Organizations increasingly develop software in a distributed manner. The cloud provides an environment to create and maintain software-based products and services. Currently, it is unknown which software processes are suited for cloud-based development and what their effects in specific contexts are.
Objective: We aim at better understanding the software process applied to distributed software development using the cloud as development environment. We further aim at providing an instrument which helps project managers comparing different solution approaches and to adapt team processes to improve future project activities and outcomes.
Method: We provide a simulation model which helps analyzing different project parameters and their impact on projects performed in the cloud. To evaluate the simulation model, we conduct different analyses using a Scrumban process and data from a project executed in Finland and Spain. An extra adaptation of the simulation model for Scrum and Kanban was used to evaluate the suitability of the simulation model to cover further process models.
Results: A comparison of the real project data with the results obtaind from the different simulation runs shows the simulation producing results close to the real data, and we could successfully replicate a distributed software project. Furthermore, we could show that the simulation model is suitable to address further process models.
Conclusion: The simulator helps reproducing activities, developers, and events in the project, and it helps analyzing potential tradeoffs, e.g., regarding throughput, total time, project size, team size and work-in-progress limits. Furthermore, the simulation model supports project managers selecting the most suitable planning alternative thus supporting decision-making processes.
Turning students into Industry 4.0 entrepreneurs: design and evaluation of a tailored study program
(2022)
Startups in the field of Industry 4.0 could be a huge driver of innovation for many industry sectors such as manufacturing. However, there is a lack of education programs to ensure a sufficient number of well-trained founders and thus a supply of such startups. Therefore, this study presents the design, implementation, and evaluation of a university course tailored to the characteristics of Industry 4.0 entrepreneurship. Educational design-based research was applied with a focus on content and teaching concept. The study program was first implemented in 2021 at a German university of applied sciences with 25 students, of which 22 participated in the evaluation. The evaluation of the study program was conducted with a pretest–posttest-design targeting three areas: (1) knowledge about the application domain, (2) entrepreneurial intention and (3) psychological characteristics. The entrepreneurial intention was measured based on the theory of planned behavior. For measuring psychological characteristics, personality traits associated with entrepreneurship were used. Considering the study context and the limited external validity of the study, the following can be identified in particular: The results show that a university course can improve participants' knowledge of this particular area. In addition, perceived behavioral control of starting an Industry 4.0 startup was enhanced. However, the results showed no significant effects on psychological characteristics.
Hardly any software development process is used as prescribed by authors or standards. Regardless of company size or industry sector, a majority of project teams and companies use hybrid development methods (short: hybrid methods) that combine different development methods and practices. Even though such hybrid methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this article, we make a first step towards a statistical construction procedure for hybrid methods. Grounded in 1467 data points from a large‐scale practitioner survey, we study the question: What are hybrid methods made of and how can they be systematically constructed? Our findings show that only eight methods and few practices build the core of modern software development. Using an 85% agreement level in the participants' selections, we provide examples illustrating how hybrid methods can be characterized by the practices they are made of. Furthermore, using this characterization, we develop an initial construction procedure, which allows for defining a method frame and enriching it incrementally to devise a hybrid method using ranked sets of practice.
With the expansion of cyber-physical systems (CPSs) across critical and regulated industries, systems must be continuously updated to remain resilient. At the same time, they should be extremely secure and safe to operate and use. The DevOps approach caters to business demands of more speed and smartness in production, but it is extremely challenging to implement DevOps due to the complexity of critical CPSs and requirements from regulatory authorities. In this study, expert opinions from 33 European companies expose the gap in the current state of practice on DevOps-oriented continuous development and maintenance. The study contributes to research and practice by identifying a set of needs. Subsequently, the authors propose a novel approach called Secure DevOps and provide several avenues for further research and development in this area. The study shows that, because security is a cross-cutting property in complex CPSs, its proficient management requires system-wide competencies and capabilities across the CPSs development and operation.
Context: Development of software intensive products and services increasingly occurs by continuously deploying product or service increments, such as new features and enhancements, to customers. Product and service developers must continuously find out what customers want by direct customer feedback and usage behaviour observation. Objective: This paper examines the preconditions for setting up an experimentation system for continuous customer experiments. It describes the RIGHT model for Continuous Experimentation (Rapid Iterative value creation Gained through High-frequency Testing), illustrating the building blocks required for such a system. Method: An initial model for continuous experimentation is analytically derived from prior work. The model is matched against empirical case study findings from two startup companies and further developed. Results: Building blocks for a continuous experimentation system and infrastructure are presented. Conclusions: A suitable experimentation system requires at least the ability to release minimum viable products or features with suitable instrumentation, design and manage experiment plans, link experiment results with a product roadmap, and manage a flexible business strategy. The main challenges are proper, rapid design of experiments, advanced instrumentation of software to collect, analyse, and store relevant data, and the integration of experiment results in both the product development cycle and the software development process.
Entrepreneurship education is becoming increasingly important in higher education and also drives the development of innovative teaching formats, which can increase student engagement. It does, however, need greater international focus to become more attractive for both domestic and international students. This paper presents the examination and course design of two case studies, which promote entrepreneurship education for domestic and international students. These examples show that entrepreneurship courses are attractive due to their focus on interdisciplinarity, experience-based learning, and project-based work. Following a design-based research approach, this paper provides a practical contribution by offering a detailed overview of course design principles, classroom practice and presents reflections and learnings from an iterative development process.
Context
In a world of high dynamics and uncertainties, it is almost impossible to have a long-term prediction of which products, services, or features will satisfy the needs of the customer. To counter this situation, the conduction of Continuous Improvement or Design Thinking for product discovery are common approaches. A major constraint in conducting product discovery activities is the high effort to discover and validate features and requirements. In addition, companies struggle to integrate product discovery activities into their agile processes and iterations.
Objective
This paper aims at suggests a supportive tool, the “Discovery Effort Worthiness (DEW) Index”, for product owners and agile teams to determine a suitable amount of effort that should be spent on Design Thinking activities. To operationalize DEW, proposals for practitioners are presented that can be used to integrate product discovery into product development and delivery.
Method
A case study was conducted for the development of the DEW index. In addition, we conducted an expert workshop to develop proposals for the integration of product discovery activities into the product development and delivery process.
Results
First, we present the "Discovery Effort Worthiness Index" in form of a formula. Second, we identified requirements that must be fulfilled for systematic integration of product discovery activities into product development and delivery. Third, we derived from the requirements proposals for the integration of product discovery activities with a company's product development and delivery.
Conclusion
The developed "Discovery Effort Worthiness Index" provides a tool for companies and their product owners to determine how much effort they should spend on Design Thinking methods to discover and validate requirements. Integrating product discovery with product development and delivery should ensure that the results of product discovery are incorporated into product development. This aims to systematically analyze product risks to increase the chance of product success.
Software process improvement (SPI) has been around for decades: frameworks are proposed, success factors are studied, and experiences have been reported. However, the sheer mass of concepts, approaches, and standards published over the years overwhelms practitioners as well as researchers. What is out there? Are there new trends and emerging approaches? What are open issues? Still, we struggle to answer these questions about the current state of SPI and related research. In this article, we present results from an updated systematic mapping study to shed light on the field of SPI, to develop a big picture of the state of the art, and to draw conclusions for future research directions. An analysis of 769 publications draws a big picture of SPI-related research of the past quarter-century. Our study shows a high number of solution proposals, experience reports, and secondary studies, but only few theories and models on SPI in general. In particular, standard SPI models like CMMI and ISO/IEC 15,504 are analyzed, enhanced, and evaluated for applicability in practice, but these standards are also critically discussed, e.g., from the perspective of SPI in small to-medium-sized companies, which leads to new specialized frameworks. New and specialized frameworks account for the majority of the contributions found (approx. 38%). Furthermore, we find a growing interest in success factors (approx. 16%) to aid companies in conducting SPI and in adapting agile principles and practices for SPI (approx. 10%). Beyond these specific topics, the study results also show an increasing interest into secondary studies with the purpose of aggregating and structuring SPI-related knowledge. Finally, the present study helps directing future research by identifying under-researched topics awaiting further investigation.
Context: An experiment-driven approach to software product and service development is gaining increasing attention as a way to channel limited resources to the efficient creation of customer value. In this approach, software capabilities are developed incrementally and validated in continuous experiments with stakeholders such as customers and users. The experiments provide factual feedback for guiding subsequent development.
Objective: This paper explores the state of the practice of experimentation in the software industry. It also identifies the key challenges and success factors that practitioners associate with the approach.
Method: A qualitative survey based on semi-structured interviews and thematic coding analysis was conducted. Ten Finnish software development companies, represented by thirteen interviewees, participated in the study.
Results: The study found that although the principles of continuous experimentation resonated with industry practitioners, the state of the practice is not yet mature. In particular, experimentation is rarely systematic and continuous. Key challenges relate to changing the organizational culture, accelerating the development cycle speed, and finding the right measures for customer value and product success. Success factors include a supportive organizational culture, deep customer and domain knowledge, and the availability of the relevant skills and tools to conduct experiments.
Conclusions: It is concluded that the major issues in moving towards continuous experimentation are on an organizational level; most significant technical challenges have been solved. An evolutionary approach is proposed as a way to transition towards experiment-driven development.