Using Regression Learners to Predict Performance Problems on Software Updates: a Case Study on Elevators Dispatching Algorithms

Remote software deployment and updating has long been common-
place in many different fields, but now, the increasing expansion of IoT and CPSoS (Cyber-Physcal System of Systems) has highlighted the need for additional mechanisms in these systems, to ensure the correct behaviour of the deployed software version after deployment. In this sense, this paper investigates the use of Machine Learning algorithms to predict acceptable behaviour in system performance of a new software release. By monitoring the real performance, eventual unexpected problems can be identified. Based on previous knowledge and actual run-time information, the proposed approach predicts the response time that can be considered acceptable for the new software release, and this information is used to identify problematic releases. The mechanism has been applied to the post-deployment monitoring of traffic algorithms in elevator systems. To evaluate the approach, we have used performance mutation testing, obtaining good results. This paper makes two contributions. First, it proposes several regression learners that have been trained with different types of traffic profiles to efficiently predict response time of the traffic dispatching algorithm. This prediction is then compared with the actual response time of the new algorithm release, and provides a verdict about its performance.Secondly, a comparison of the different learners is performed.