Towards a goal‐driven approach to action selection in self‐adaptive software

Self‐adaptive software is a closed‐loop system, since it continuously monitors its context (i.e. environment) and/or self (i.e. software entities) in order to adapt itself properly to changes. We believe that representing adaptation goals explicitly and tracing them at run‐time are helpful in decision making for adaptation. While goal‐driven models are used in requirements engineering, they have not been utilized systematically yet for run‐time adaptation. To address this research gap, this article focuses on the deciding process in self‐adaptive software, and proposes the Goal‐Action‐Attribute Model (GAAM). An action selection mechanism, based on cooperative decision making, is also proposed that uses GAAM to select the appropriate adaptation action(s). The emphasis is on building a light‐weight and scalable run‐time model which needs less design and tuning effort comparing with a typical rule‐based approach. The GAAM and action selection mechanism are evaluated using a set of experiments on a simulated multi‐tier enterprise application, and two sample ordinal and cardinal action preference lists. The evaluation is accomplished based on a systematic design of experiment and a detailed statistical analysis in order to investigate several research questions. The findings are promising, considering the obtained results, and other impacts of the approach on engineering self‐adaptive software. Although, one case study is not enough to generalize the findings, and the proposed mechanism does not always outperform a typical rule‐based approach, less effort, scalability, and flexibility of GAAM are remarkable. Copyright © 2011 John Wiley & Sons, Ltd.     

Automating trade-off analysis of security requirements

A key aspect of engineering secure systems is identifying adequate security requirements to protect critical assets from harm. However, security requirements may compete with other requirements such as cost and usability. For this reason, they may only be satisfied partially and must be traded off against other requirements to achieve “good-enough security”. This paper proposes a novel approach to automate security requirements analysis in order to determine maximum achievable satisfaction level for security requirements and identify trade-offs between security and other requirements. We also propose a pruning algorithm to reduce the search space size in the analysis. We represent security concerns and requirements using asset, threat, and goal models, initially proposed in our previous work. To deal with uncertainty and partial requirements, satisfaction security concerns are quantified by leveraging the notion of composite indicators, which are computed through metric functions based on range normalisation. An SMT solver (Z3) interprets the models and automates the execution of our analyses. We illustrate and evaluate our approach by applying it to a substantive example of a service-based application for exchanging emails.