Search-Based Synthesis of Probabilistic Models for Quality-of-Service Software Engineering

The formal verification of finite-state probabilistic models supports the engineering of software with strict quality-of-service (QoS) requirements. However, its use in software design is currently a tedious process of manual multiobjective optimisation. Software designers must build and verify probabilistic models for numerous alternative architectures and instantiations of the system parameters. When successful, they end up with feasible but often suboptimal models. The EvoChecker search-based software engineering approach and tool introduced in our paper employ multiobjective optimisation genetic algorithms to automate this process and considerably improve its outcome. We evaluate EvoChecker for six variants of two software systems from the domains of dynamic power management and foreign exchange trading. These systems are characterised by different types of design parameters and QoS requirements, and their design spaces comprise between 2E+14 and 7.22E+86 relevant alternative designs. Our results provide strong evidence that EvoChecker significantly outperforms the current practice and yields actionable insights for software designers

Synthesis of Probabilistic Models for Quality-of-Service Software Engineering

An increasingly used method for the engineering of software systems with strict quality-of-service (QoS) requirements involves the synthesis and verification of probabilistic models for many alternative architectures and instantiations of system parameters. Using manual trial-and-error or simple heuristics for this task often produces suboptimal models, while the exhaustive synthesis of all possible models is typically intractable. The EvoChecker search-based software engineering approach presented in our paper addresses these limitations by employing evolutionary algorithms to automate the model synthesis process and to significantly improve its outcome. EvoChecker can be used to synthesise the Pareto-optimal set of probabilistic models associated with the QoS requirements of a system under design, and to support the selection of a suitable system architecture and configuration. EvoChecker can also be used at runtime, to drive the efficient reconfiguration of a self-adaptive software system. We evaluate EvoChecker on several variants of three systems from different application domains, and show its effectiveness and applicability