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

Supply Chain Planning with Incremental Development, Modular Design, and Evolutionary Updates

Proceedings Paper (for Acquisition Research Program)The policy specified by DoDI 5000.02 (DoD, 2008, December 8) prescribes an evolutionary acquisition strategy. Products with long lifecycles such as torpedoes, evolutionary updates via incremental development, modular design updates, technology refreshes, technology insertions, and Advanced Processor Builds are all in play at the same time. Various functional elements of the weapon system are often redesigned during the lifecycle to meet evolving requirements. Component obsolescence and failures must also be anticipated and addressed in upgrade planning. Within each weapon system''s evolutionary acquisition, cycle-changing requirements may expose weaknesses that have to be rectified across the inventory. New acquisition paradigms such as modular design have to be introduced into the supply chain while maintaining inventory levels of previously designed weapons at a high level of readiness. Thus, a diverse set of requirements must be satisfied with a finite set of resources. The acquisition policy does not provide guidance on how to address cross-coordination and optimization of project resources. This paper explores decision models for balancing conflicting demands and discusses the application of how these models address cross-coordination and optimization of project resources in the torpedo acquisition process while keeping the weapon''s efficiency and inventory effectiveness at or above minimum specified levels.Naval Postgraduate School Acquisition Research ProgramApproved for public release; distribution is unlimited

Technology and carbon mitigation in developing countries: Are cleaner coal technologies a viable option?

human development, climate change

Analyzing Uncertainty in Release Planning: A Method and Experiment for Fixed-Date Release Cycles

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 in estimating software development effort and business value. The article’s objective is to study whether analyzing uncertainty during release planning generates better release plans than if uncertainty is ignored. To study this question, we have developed a novel release planning method under uncertainty, called BEARS, that models uncertainty using Bayesian probability distributions and recommends release plans that maximize expected net present value and expected punctuality. We then compare release plans recommended by BEARS to those recommended by methods that ignore uncertainty on 32 release planning problems. The experiment shows that BEARS recommends release plans with higher expected net present value and expected punctuality than methods that ignore uncertainty, thereby indicating the harmful effects of ignoring uncertainty during release planning. These results highlight the importance of eliciting and analyzing uncertainty in software effort and value estimations and call for increased research in these areas

Benefits and costs of nitrogen fertilizer management for climate change mitigation: Focus on India and Mexico.

This report analyzes the costs and benefits of managing nitrogen fertilizer in ways that also reduce greenhouse gas emissions in cereal production (rice, wheat, and maize) in India and Mexico. The purpose of this work is to inform finance needed for low emissions agricultural development. For each agricultural mitigation practice identified, the corresponding potential emissions reduction and on-farm costs and benefits (e.g., operational costs, savings, or other benefits) are provided, based on available literature

CHARTING PROGRESS IN THE SOFTWARE ACQUISITION PATHWAY

The Department of the Navy (DON) recently implemented the Department of Defense (DOD) Software Acquisition Pathway (SWP), a software acquisition strategy for custom application and embedded software. The purpose of the SWP is to enable rapid and iterative delivery of high-priority software capability to the intended user. But while the SWP uses an agile software development approach, neither the DOD nor the DON have yet provided comprehensive governance tools and methods for SWP programs to iteratively plan, track, and assess acquisition outcomes in agile environments. To close this gap, the author systematically researched commercial software engineering management and digital product development practices as well as prior DOD software acquisition reform studies. Based on the results, the author showed that Earned Value Management is incompatible with the SWP and recommended alternative techniques to measure cost and schedule performance. Additionally, the author recommended a phased approach to manage DON SWP custom application programs, whereby a minimal, unitless work breakdown structure is used to track progress until demonstrating the minimum viable product to the user in a testing environment; product-based metrics are then tracked until initial release of the custom application software; and then outcome-based goals are iteratively set, tracked, and assessed using the Objectives and Key Results framework for as long as the custom application software is in use.Captain, United States Air ForceApproved for public release. Distribution is unlimited

Dynamic order acceptance and capacity planning within a multi-project environment.

We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for regular as well as nonregular capacity.We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as an optimal capacity allocation for accepted projects, both with an eye on maximizing the expected revenues within the problem horizon.We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selectionand capacity usage.Capacity planning; multi-project; Order acceptance; Stochastic dynamic programming;