Requirements Prioritisation and Retrospective Analysis for Release Planning Process Improvement

The quality of a product can be defined by its ability to satisfy the needs and expectations of its customers. Achieving quality is especially difficult in market-driven situations since the product is released on an open market with numerous potential customers and users with various wishes. The quality of the software product is to a large extent determined by the quality of the requirements engineering (RE) and release planning decisions regarding which requirements that are selected for a product. The goal of this thesis is to enhance software product quality and increase the competitive edge of software organisations by improving release planning decision-making. The thesis is based on empirical research, including both qualitative and quantitative research approaches. The research contains a qualitative survey of RE challenges in market-driven organisations based on interviews with practitioners. The survey provided increased understanding of RE challenges in the software industry and gave input to the continued research. Among the challenging issues, one was selected for further investigation due to its high relevance to the practitioners: requirements prioritisation and release planning decision-making. Requirements prioritisation techniques were evaluated through experiments, suggesting that ordinal scale techniques based on grouping and ranking may be valuable to practitioners. Finally, a retrospective method called PARSEQ (Post-release Analysis of Requirements SElection Quality) is introduced and tested in three case studies. The method aims at evaluating prior releases and finding improvement proposals for release planning decision-making in future release projects. The method was found valuable by all participants and relevant improvement proposals were discovered in all cases

Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations

The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav

From a Competition for Self-Driving Miniature Cars to a Standardized Experimental Platform: Concept, Models, Architecture, and Evaluation

Context: Competitions for self-driving cars facilitated the development and research in the domain of autonomous vehicles towards potential solutions for the future mobility. Objective: Miniature vehicles can bridge the gap between simulation-based evaluations of algorithms relying on simplified models, and those time-consuming vehicle tests on real-scale proving grounds. Method: This article combines findings from a systematic literature review, an in-depth analysis of results and technical concepts from contestants in a competition for self-driving miniature cars, and experiences of participating in the 2013 competition for self-driving cars. Results: A simulation-based development platform for real-scale vehicles has been adapted to support the development of a self-driving miniature car. Furthermore, a standardized platform was designed and realized to enable research and experiments in the context of future mobility solutions. Conclusion: A clear separation between algorithm conceptualization and validation in a model-based simulation environment enabled efficient and riskless experiments and validation. The design of a reusable, low-cost, and energy-efficient hardware architecture utilizing a standardized software/hardware interface enables experiments, which would otherwise require resources like a large real-scale test track.Comment: 17 pages, 19 figues, 2 table