Using a Machine Learning Approach to Implement and Evaluate Product Line Features

Bike-sharing systems are a means of smart transportation in urban environments with the benefit of a positive impact on urban mobility. In this paper we are interested in studying and modeling the behavior of features that permit the end user to access, with her/his web browser, the status of the Bike-Sharing system. In particular, we address features able to make a prediction on the system state. We propose to use a machine learning approach to analyze usage patterns and learn computational models of such features from logs of system usage. On the one hand, machine learning methodologies provide a powerful and general means to implement a wide choice of predictive features. On the other hand, trained machine learning models are provided with a measure of predictive performance that can be used as a metric to assess the cost-performance trade-off of the feature. This provides a principled way to assess the runtime behavior of different components before putting them into operation.Comment: In Proceedings WWV 2015, arXiv:1508.0338

Enhancing Test Coverage by Back-tracing Model-checker Counterexamples

AbstractThe automatic detection of unreachable coverage goals and generation of tests for "corner-case" scenarios is crucial to make testing and simulation based verification more effective. In this paper we address the problem of coverability analysis and test case generation in modular and component based systems. We propose a technique that, given an uncovered branch in a component, either establishes that the branch cannot be covered or produces a test case at the system level which covers the branch. The technique is based on the use of counterexamples returned by model checkers, and exploits compositionality to cope with large state spaces typical of real applications

Applying generalized non deducibility on compositions (GNDC) approach in dependability

This paper presents a framework where dependable systems can be uniformly modeled and dependable properties analyzed within the Generalized Non Deducibility on Compositions (GNDC), a scheme that has been profitably used in definition and analysis of security properties. Precisely, our framework requires a systems to be modelled using a formal calculus, here the CCS process algebra, where both the failing behaviour of the system and the related fault-recovering procedures are also explicitly described. An environment able to inject any fault in the system is then defined as a separated component. The parallel composition between the system and the environment represents our scenario of analysis, where some fault tolerance property (e.g., fail stop, safe and silent) are studied as instances of GNDC properties. By using different instances of GNDC we are able to argue about the availability of effective methodologies of analysis, and on the possibility of applying compositional techniques

Hacking an Ambiguity Detection Tool to Extract Variation Points: an Experience Report

Natural language (NL) requirements documents can be a precious source to identify variability information. This information can be later used to define feature models from which different systems can be instantiated. In this paper, we are interested in validating the approach we have recently proposed to extract variability issues from the ambiguity defects found in NL requirement documents. To this end, we single out ambiguities using an available NL analysis tool, QuARS, and we classify the ambiguities returned by the tool by distinguishing among false positives, real ambiguities, and variation points. We consider three medium sized requirement documents from different domains, namely, train control, social web, home automation. We report in this paper the results of the assessment. Although the validation set is not so large, the results obtained are quite uniform and permit to draw some interesting conclusions. Starting from the results obtained, we can foresee the tailoring of a NL analysis tool for extracting variability from NL requirement documents

Product Lines for Service Oriented Applications - PL for SOA

Comment: In Proceedings WWV 2011, arXiv:1108.208

A state/event-based model-checking approach for the analysis of abstract system properties.

AbstractWe present the UMC framework for the formal analysis of concurrent systems specified by collections of UML state machines. The formal model of a system is given by a doubly labelled transition system, and the logic used to specify its properties is the state-based and event-based logic UCTL. UMC is an on-the-fly analysis framework which allows the user to interactively explore a UML model, to visualize abstract behavioural slices of it and to perform local model checking of UCTL formulae. An automotive scenario from the service-oriented computing (SOC) domain is used as case study to illustrate our approach