VaryMinions

From business processes to course management, variability-intensive software systems (VIS) are now ubiquitous. Such systems can configure their behaviours through the activation of options, e.g., to derive variants handling building permits across municipalities, or implementing different functionalities(quizzes, forums) for a given course. These customisation facilities allow VIS to support a variety of relevant customer requirements while taking advantage of reuse for common parts, thus realising both scope and scale economies.Behavioural differences amongst variants manifest themselves in event logs.Should an anomalous behaviour happen or a refactoring of the system needed, it is mandatory to know which variant(s) have produced this behaviour. Since variant information is barely present in logs, this paper supports this task by employing machine learning techniques to map behaviours (event sequences)to variants. Specifically, we train Long Short Term Memory (LSTMs) andGated Recurrent Units (GRUs), two kinds of recurrent neural networks, to relate event sequences with the variants they belong to on six different datasets issued from the configurable process and VIS domains. Our results, after having evaluated 20 different architectures of LSTM/GRU, demonstrate that it is possible to learn effectively the trace-to-variant mapping with a good accuracy(at least 80% and up to 99%) and at scale, i.e., identifying 50 variants using5000+ traces for each variant.