Requirements-driven evolution of sociotechnical systems via probabilistic reasoning and hill climbing

Abstract

Sociotechnical systems (STSs) are defined by the interaction between technical systems, like software and machines, and social entities, like humans and organizations. The entities within an STS are autonomous, thus weakly controllable, and the environment where the STS operates is highly dynamic. As a result, the design artifacts that represent the requirements of an STS, such as requirements models, may end up being invalid when the system operates, for the autonomous entities do not comply with the requirements, or the environment changes. In this paper, we present a framework that uses runtime execution data to support the runtime validation of requirements models and to guide the evolution of an STS. We propose two types of evolution: (i) manual: the analyst uses Bayesian inference to discover which assumptions in a requirements model are invalid and manually adjusts the system or its model; and (ii) automated: requirements are iteratively revised by an hill climbing algorithm searching for requirements that maximize the achievement of the stakeholders' objectives. We evaluate the effectiveness of different revision heuristics on a smart traffic simulation applied to an exemplar from the self-adaptive systems literature. The results show that our heuristics, informed by runtime execution data, outperform standard uninformed heuristics, in terms of convergence speed, solution quality, and stability. Moreover, the algorithms show good resilience to noise introduced into the execution data.

Transcendental Logic-Based Formalism for Semantic Representation of Software Project Requirements Architecture

“…However, the constantly increasing software requirements complexity led, on the one hand, to the emergence of technologies such as Kubernetes (Lukša, 2017) for deploying and scaling applications and, on the other hand, must force researchers and practitioners more actively use artificial intelligence technologies to overcome challenges. A typical example of this trend is found in the field of Search-Based Software Engineering (e.g., Harman & Chicano, 2015;Ruchika et al, 2017;Ramí rez et al, 2019), as well as works in the field of using probabilistic reasoning and machine learning in the software life cycle (Balikuddembe et al, 2009;Pandey et al, 2021;Jayagopal et al, 2021;Xu et al, 2016;Dell' Anna et al, 2019). The most popular intelligent techniques for software development are as follows: reasoning under uncertainty (mainly, Bayesian network), search-based solutions, and machine learning (Perkusich et al, 2020).…”

Self-Adaptive Model Based on Goal-Oriented Requirements Engineering for Handling Service Variability

“…Our proposed model is not only prepared for handling changes, but accomodates system evolution requirements as the growth of service items also becomes one of the actions of the monitoring results. Anna et al (2019), proposed a model of requirements engineering for adaptive systems based on goal model, and Mendoca et al (2016) proposed a model of contextualed runtime goal through a probabilistic approach. However, the dynamic evolution requirements are still not covered in these works, while our model provides this capability through the plug and play model.…”