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Software development consists to a large extend of humanbased processes with continuously increasing demands regarding interdisciplinary team work. Understanding the dynamics of software teams can be seen as highly important to successful project execution. Hence, for future project managers, knowledge about non-technical processes in teams is significant. In this paper, we present a course unit that provides an environment in which students can learn and experience the impact of group dynamics on project performance and quality. The course unit uses the Tuckman model as theoretical framework, and borrows from controlled experiments to organize and implement its practical parts in which students then experience the effects of, e.g., time pressure, resource bottlenecks, staff turnover, loss of key personnel, and other stress factors. We provide a detailed design of the course unit to allow for implementation in further software project management courses. Furthermore, we provide experiences obtained from two instances of this unit conducted in Munich and Karlskrona with 36 graduate students. We observed students building awareness of stress factors and developing counter measures to reduce impact of those factors. Moreover, students experienced what problems occur when teams work under stress and how to form a performing team despite exceptional situations.
Using measurement and simulation for understanding distributed development processes in the Cloud
(2017)
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 widely unknown which software processes are suited for Cloud-based development and what their effects in specific contexts are. This paper presents a process simulation to study distributed development in the Cloud. We contribute a simulation model, which helps analyzing different project parameters and their impact on projects carried out in the Cloud. The simulator helps reproducing activities, developers, issues and events in the project, and it generates statistics, e.g., on throughput, total time, and lead and cycle time. The aim of this simulation model is thus to analyze the tradeoffs regarding throughput, total time, project size, and team size. Furthermore, the modified simulation model aims to help project managers select the most suitable planning alternative. Based on observed projects in Finland and Spain, we simulated a distributed project using artificial and real data. Particularly, we studied the variables project size, team size, throughput, and total project duration. A comparison of the real project data with the results obtained from the simulation shows the simulation producing results close to the real data, and we could successfully replicate a distributed software project. By improving the understanding of distributed development processes, our simulation model thus supports project managers in their decision-making.
For large-scale processes as implemented in organizations that develop software in regulated domains, comprehensive software process models are implemented, e.g., for compliance requirements. Creating and evolving such processes is demanding and requires software engineers having substantial modeling skills to create consistent and certifiable processes. While teaching process engineering to students, we observed issues in providing and explaining models. In this paper, we present an exploratory study in which we aim to shed light on the challenges students face when it comes to modeling. Our findings show that students are capable of doing basic modeling tasks, yet, fail in utilizing models correctly. We conclude that the required skills, notably abstraction and solution development, are underdeveloped due to missing practice and routine. Since modeling is key to many software engineering disciplines, we advocate for intensifying modeling activities in teaching.
Software process improvement (SPI) is 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 emerging approaches? What are open issues? Still, we struggle to answer the question for what is the current state of SPI and related research? In this paper, we present initial results from a systematic mapping study to shed light on the field of SPI and to draw conclusions for future research directions. An analysis of 635 publications draws a big picture of SPI-related research of the past 25 years. Our study shows a high number of solution proposals, experience reports, and secondary studies, but only few theories. In particular, standard SPI models like CMMI and ISO/IEC 15504 are analyzed, enhanced, and evaluated for applicability, whereas these standards are critically discussed from the perspective of SPI in small-to- medium-sized companies, which leads to new specialized frameworks. Furthermore, we find a growing interest in success factors to aid companies in conducting SPI.
First International Workshop on Hybrid dEveLopmENt Approaches in Software Systems Development
(2017)
A software process is the game plan to organize project teams and run projects. Yet, it still is a challenge to select the appropriate development approach for the respective context. A multitude of development approaches compete for the users’ favor, but there is no silver bullet serving all possible setups. Moreover, recent research as well as experience from practice shows companies utilizing different development approaches to assemble the bestfitting approach for the respective company: a more traditional process provides the basic framework to serve the organization, while project teams embody this framework with more agile (and/or lean) practices to keep their flexibility. The first HELENA workshop aims to bring together the community to discuss recent findings and to steer future work.
Selecting a suitable development method for a specific project context is one of the most challenging activities in process design. Every project is unique and, thus, many context factors have to be considered. Recent research took some initial steps towards statistically constructing hybrid development methods, yet, paid little attention to the peculiarities of context factors influencing method and practice selection. In this paper, we utilize exploratory factor analysis and logistic regression analysis to learn such context factors and to identify methods that are correlated with these factors. Our analysis is based on 829 data points from the HELENA dataset. We provide five base clusters of methods consisting of up to 10 methods that lay the foundation for devising hybrid development methods. The analysis of the five clusters using trained models reveals only a few context factors, e.g., project/product size and target application domain, that seem to significantly influence the selection of methods. An extended descriptive analysis of these practices in the context of the identified method clusters also suggests a consolidation of the relevant practice sets used in specific project contexts.