A Framework Based on Learning Techniques for Decision-making in Self-adaptive Software

The development of Self-adaptive Software (SaS) presents specific innovative features compared to traditional ones since this type of software constantly deals with structural and/or behavioral changes at runtime. Capabilities of human administration are showing a decrease in relative effectiveness, since some tasks have been difficult to manage introducing potential problems, such as change management and simple human error. Self-healing systems, a system class of SaS, have emerged as a feasible solution in contrast to management complexity, since such system often combines machine learning techniques with control loops to reduce the number of situations requiring human intervention. This paper presents a framework based on learning techniques and the control loop (MAPE-K) to support the decision-making activity for SaS. In addition, it is noteworthy that this framework is part of a wider project developed by the authors of this paper in previous work (i.e., reference architecture for SaS [1]). Aiming to present the viability of our framework, we have conducted a case study using a flight plan module for Unmanned Aerial Vehicles. The results have shown an environment accuracy of about 80%, enabling us to project good perspectives of contribution to the SaS area and other domains of software systems, and enabling knowledge sharing and technology transfer from academia to industry. Keywords-Self-adaptive software; Reference Architecture; Framework; Learning Techniques; Decision-making.