Steve Versteeg

Muhammad Ashad Kabir, Jun Han, Md. Arafat Hossain et al.

Software behavioral models have proven useful for emulating and testing software systems. Many techniques have been proposed to infer behavioral models of software systems from their interaction traces. The quality of the inferred model is critical to its successful use. While generalization is necessary to deduce concise behavioral models, existing techniques of inferring models, in general, overgeneralize what behavior is valid. Imprecise models include many spurious behaviors, and thus compromise the effectiveness of their use. In this paper, we propose a novel technique that increases the accuracy of the behavioral model inferred from interaction traces. The essence of our approach is a heuristic-based generalization and truthful minimization. The set of heuristics include patterns to match input traces and generalize them towards concise model representations. Furthermore, we adopt a truthful minimization technique to merge these generalized traces. The key insight of our approach is to infer a concise behavioral model without compromising its accuracy. We present an empirical evaluation of how our approach improves upon the state-of-the-art specification inference techniques. The results show that our approach mines model with 100% precision and recall with a limited computation overhead.

Thilini Bhagya, Jens Dietrich, Hans Guesgen et al.

We present GHTraffic, a dataset of significant size comprising HTTP transactions extracted from GitHub data and augmented with synthetic transaction data. The dataset facilitates reproducible research on many aspects of service-oriented computing. This paper discusses use cases for such a dataset and extracts a set of requirements from these use cases. We then discuss the design of GHTraffic, and the methods and tool used to construct it. We conclude our contribution with some selective metrics that characterise GHTraffic.

Zeinab Farahmandpour, Steve Versteeg, Anand Kameswaran et al.

Service virtualization is an approach that uses virtualized environments to automatically test enterprise services in production-like conditions. Many techniques have been proposed to provide such a realistic environment for enterprise services. The Internet-of-Things (IoT) is an emerging field which connects a diverse set of devices over different transport layers, using a variety of protocols. Provisioning a virtual testbed of IoT devices can accelerate IoT application development by enabling automated testing without requiring a continuous connection to the physical devices. One solution is to expand existing enterprise service virtualization to IoT environments. There are various structural differences between the two environments that should be considered to implement appropriate service virtualization for IoT. This paper examines the structural differences between various IoT protocols and enterprise protocols and identifies key technical challenges that need to be addressed to implement service virtualization in IoT environments.

Jean-Guy Schneider, Peter Mandile, Steve Versteeg

Assuring quality of contemporary software systems is a very challenging task due to the often large complexity of the deployment environments in which they will operate. Service virtualization is an approach to this challenge where services within the deployment environment are emulated by synthesising service response messages from models or by recording and then replaying service interaction messages with the system. Record-and-replay techniques require an approach where (i) message prototypes can be derived from recorded system interactions (i.e. request-response sequences), (ii) a scheme to match incoming request messages against message prototypes, and (iii) the synthesis of response messages based on similarities between incoming messages and the recorded system interactions. Previous approaches in service virtualization have required a multiple sequence alignment (MSA) algorithm as a means of finding common patterns of similarities and differences between messages required by all three steps. In this paper, we present a novel MSA algorithm based on Generalized Suffix Trees (GSTs). We evaluated the accuracy and efficiency of the proposed algorithm against six enterprise service message trace datasets, with the proposed algorithm performing up to 50 times faster than standard MSA approaches. Furthermore, the algorithm has applicability to other domains beyond service virtualization.

Cameron Hine, Jean-Guy Schneider, Jun Han et al.

Constructing testbeds for systems which are interconnected with large networks of other software services is a challenging task. It is particularly difficult to create testbeds facilitating evaluation of the non-functional qualities of a system, such as scalability, that can be expected in production deployments. Software service emulation is an approach for creating such testbeds where service behaviour is defined by emulate-able models executed in an emulation runtime environment. We present (i) a meta-modelling framework supporting emulate-able service modelling (including messages, protocol, behaviour and states), and (ii) Kaluta, an emulation environment able to concurrently execute large numbers (thousands) of service models, providing a testbed which mimics the behaviour and characteristics of large networks of interconnected software services. Experiments show that Kaluta can emulate 10,000 servers using a single physical machine, and is a practical testbed for scalability testing of a real, enterprise-grade identity management suite. The insights gained into the tested enterprise system were used to enhance its design.

Steve Versteeg, Miao Du, Jean-Guy Schneider et al.

Large enterprise software systems make many complex interactions with other services in their environment. Developing and testing for production-like conditions is therefore a very challenging task. Current approaches include emulation of dependent services using either explicit modelling or record-and-replay approaches. Models require deep knowledge of the target services while record-and-replay is limited in accuracy. Both face developmental and scaling issues. We present a new technique that improves the accuracy of record-and-replay approaches, without requiring prior knowledge of the service protocols. The approach uses Multiple Sequence Alignment to derive message prototypes from recorded system interactions and a scheme to match incoming request messages against prototypes to generate response messages. We use a modified Needleman-Wunsch algorithm for distance calculation during message matching. Our approach has shown greater than 99% accuracy for four evaluated enterprise system messaging protocols. The approach has been successfully integrated into the CA Service Virtualization commercial product to complement its existing techniques.

Steve Versteeg, Miao Du, John Bird et al.

Service virtualisation is a supporting tool for DevOps to generate interactive service models of dependency systems on which a system-under-test relies. These service models allow applications under development to be continuously tested against production-like conditions. Generating these virtual service models requires expert knowledge of the service protocol, which may not always be available. However, service models may be generated automatically from network traces. Previous work has used the Needleman-Wunsch algorithm to select a response from the service model to play back for a live request. We propose an extension of the Needleman-Wunsch algorithm, which uses entropy analysis to automatically detect the critical matching fields for selecting a response. Empirical tests against four enterprise protocols demonstrate that entropy weighted matching can improve response accuracy.

Miao Du, Steve Versteeg, Jean-Guy Schneider et al.

Enterprise software systems make complex interactions with other services in their environment. Developing and testing for production-like conditions is therefore a challenging task. Prior approaches include emulations of the dependency services using either explicit modelling or record-and-replay approaches. Models require deep knowledge of the target services while record-and-replay is limited in accuracy. We present a new technique that improves the accuracy of record-and-replay approaches, without requiring prior knowledge of the services. The approach uses multiple sequence alignment to derive message prototypes from recorded system interactions and a scheme to match incoming request messages against message prototypes to generate response messages. We introduce a modified Needleman-Wunsch algorithm for distance calculation during message matching, wildcards in message prototypes for high variability sections, and entropy-based weightings in distance calculations for increased accuracy. Combined, our new approach has shown greater than 99% accuracy for four evaluated enterprise system messaging protocols.