Data structure selection and tuning is laborious but can vastly improve an application’s performance and memory footprint. Some data structures share a common interface and enjoy multiple implementations. We call them Darwinian Data Structures (DDS), since we can subject their implementations to survival of the fittest. We introduce ARTEMIS a multi-objective, cloud-based search-based optimisation framework that automatically finds optimal, tuned DDS modulo a test suite, then changes an application to use that DDS. ARTEMIS achieves substantial performance improvements for every project in 5 Java projects from DaCapo benchmark, 8 popular projects and 30 uniformly sampled projects from GitHub. For execution time, CPU usage, and memory consumption, ARTEMIS finds at least one solution that improves all measures for 86% (37/43) of the projects. The median improvement across the best solutions is 4.8%, 10.1%, 5.1% for runtime, memory and CPU usage.

Uncertainty is characterised by incomplete understanding. It is inevitable in the early phase of requirements engineering, and can lead to unsound requirement decisions. Inappropriate requirement choices may result in products that fail to satisfy stakeholders' needs, and might cause loss of revenue. To overcome uncertainty, requirements engineering decision support needs uncertainty management. In this research, we develop a decision support framework METRO for the Next Release Problem (NRP) to manage algorithmic uncertainty and requirements uncertainty. An exact NRP solver (NSGDP) lies at the heart of METRO. NSGDP's exactness eliminates interference caused by approximate existing NRP solvers. We apply NSGDP to three NRP instances, derived from a real world NRP instance, RALIC, and compare with NSGA-II, a widely-used approximate (inexact) technique. We find the randomness of NSGA-II results in decision makers missing up to 99.95 percent of the optimal solutions and obtaining up to 36.48 percent inexact requirement selection decisions. The chance of getting an inexact decision using existing approximate approaches is negatively correlated with the implementation cost of a requirement (Spearman r up to -0.72). Compared to the inexact existing approach, NSGDP saves 15.21 percent lost revenue, on average, for the RALIC dataset.

Uncertainty is inevitable in real world requirement engineering. It has a significant impact on the feasibility of proposed solutions and thus brings risks to the software release plan. This paper proposes a multi-objective optimization technique, augmented with Monte-Carlo Simulation, that optimizes requirement choices for the three objectives of cost, revenue, and uncertainty. The paper reports the results of an empirical study over four data sets derived from a single real world data set. The results show that the robust optimal solutions obtained by our approach are conservative compared to their corresponding optimal solutions produced by traditional Multi-Objective Next Release Problem. We obtain a robustness improvement of at least 18% at a small cost (a maximum 0.0285 shift in the 2D Pareto-front in the unit space). Surprisingly we found that, though a requirement's cost is correlated with inclusion on the Pareto-front, a requirement's expected revenue is not.