Bridging the gap between research and agile practice: an evolutionary model

There is wide acceptance in the software engineering field that industry and research can gain significantly from each other and there have been several initiatives to encourage collaboration between the two. However there are some often-quoted challenges in this kind of collaboration. For example, that the timescales of research and practice are incompatible, that research is not seen as relevant for practice, and that research demands a different kind of rigour than practice supports. These are complex challenges that are not always easy to overcome. Since the beginning of 2013 we have been using an approach designed to address some of these challenges and to bridge the gap between research and practice, specifically in the agile software development arena. So far we have collaborated successfully with three partners and have investigated three practitioner-driven challenges with agile. The model of collaboration that we adopted has evolved with the lessons learned in the first two collaborations and been modified for the third. In this paper we introduce the collaboration model, discuss how it addresses the collaboration challenges between research and practice and how it has evolved, and describe the lessons learned from our experience

A Bayesian Approach for Software Release Planning under Uncertainty

Release planning — deciding what features to implement in upcoming releases of a software system— is a critical activity in iterative software development.Many release planning methods exist but most ignore the inevitable uncertainty of future development effort and business value. The thesis investigates how to analyse uncertainty during release planning and whether analysing uncertainty leads to better decisions than if uncertainty is ignored. The thesis’s first contribution is a novel release planning method designed to analyse uncertainty in the context of the Incremental Funding Method, an incremental cost-value based approach to software development. Our method uses triangular distributions, Monte-Carlo simulation and multi-objective optimisation to shortlist release plans that maximise expected net present value and minimise investment cost and risk. The second contribution is a new release planning method, called BEARS, designed to analyse uncertainty in the context of fixed-date release processes.Fixed-date release processes are more common in industry than fixed-scope release processes. BEARS models uncertainty about feature development time and economic value using lognormal distributions. It then uses Monte-Carlo simulation and search-based multi-objective optimisation to shortlist release plans that maximise expected net present value and expected punctuality. The method helps release planners explore possible tradeoffs between these two objectives. The thesis’ third contribution is an experiment to study whether analysing uncertainty using BEARS leads to shortlisting better release plans than if uncertainty is ignored, or if uncertainty is analysed assuming fixed-scope releases. The experiment compares 5 different release planning models on 32 release planning problems.The results show that analysing uncertainty using BEARS leads to shortlisting release plans with higher expected net present value and higher expected punctuality than methods that ignore uncertainty or that assume fixed-scope releases.Our experiment therefore shows that analysing uncertainty can lead to better release planning decisions than if uncertainty is ignored

Situating the Next Generation of Impact Measurement and Evaluation for Impact Investing

In taking stock of the landscape, this paper promotes a convergence of methods, building from both the impact investment and evaluation fields.The commitment of impact investors to strengthen the process of generating evidence for their social returns alongside the evidence for financial returns is a veritable game changer. But social change is a complex business and good intentions do not necessarily translate into verifiable impact.As the public sector, bilaterals, and multilaterals increasingly partner with impact investors in achieving collective impact goals, the need for strong evidence about impact becomes even more compelling. The time has come to develop new mindsets and approaches that can be widely shared and employed in ways that will advance the frontier for impact measurement and evaluation of impact investing. Each of the menu options presented in this paper can contribute to building evidence about impact. The next generation of measurement will be stronger if the full range of options comes into play and the more evaluative approaches become commonplace as means for developing evidence and testing assumptions about the processes of change from a stakeholder perspective– with a view toward context and systems.Creating and sharing evidence about impact is a key lever for contributing to greater impact, demonstrating additionality, and for building confidence among potential investors, partners and observers in this emergent industry on its path to maturation. Further, the range of measurement options offers opportunities to choose appropriate approaches that will allow data to contribute to impact management– to improve on the business model of ventures and to improve services and systems that improve conditions for people and households living in poverty.

Innovation Ecosystems: A Sustainability Perspective

To be competitive, companies must develop capabilities that allow them to react rapidly to market demands. The innovation methods of the past are not adapted to the turbulence of the modern world. In the last decade, increasing globalization of markets and Industry 4.0 have caused profound changes in the best way to manage the innovation process. This e-book includes a collection of thirteen papers that discuss theoretical approaches, case studies, and surveys focused on issues related to open innovation and its mechanisms