Högskolan i Skövde

Within a community, social relationships are paramount to profile individuals’ conduct. For instance, an individual within a social network might be compelled to embrace a behaviour that his/her companion has recently adopted. Such social attitude is labelled social influence, which assesses the extent by which an individual’s social neighbourhood adopt that individual’s behaviour. We suggest an original approach to influence maximization using a fuzzy-logic based model, which combines influence-weights associated with historical logs of the social network users, and their favourable location in the network. Our approach uses a two-phases process to maximise influence diffusion. First, we harness the complexity of the problem by partitioning the network into significantly-enriched community-structures, which we then use as modules to locate the most influential nodes across the entire network. These key users are determined relatively to a fuzzy-logic based technique that identifies the most influential users, out of which the seed-set candidates to diffuse a behaviour or an innovation are extracted following the allocated budget for the influence campaign. This way to deal with influence propagation in social networks, is different from previous models, which do not compare structural and behavioural attributes among members of the network. The performance results show the validity of the proposed partitioning-approach of a social network into communities, and its contribution to “activate” a higher number of nodes overall. Our experimental study involves both empirical and real contemporary social-networks, whereby a smaller seed set of key users, is shown to scale influence to the high-end compared to some renowned techniques, which employ a larger seed set of key users and yet they influence less nodes in the social network.

Redundant mutants might cause problems when benchmarking since testing techniques can get high scores without detecting any nonredundant mutants. However, removing nonredundant mutants might cause similar problems. Subsumed mutants are per definition also redundant since no additional tests are required to detect them once all other mutants are detected. We focus on relational operator replacement (ROR) and conditional operator replacement mutants. Subsumption relations between ROR mutants are defined by fault hierarchies. The fault hierarchies are proven for weak mutation but have since they were published been used with strong mutation. We prove that ROR fault hierarchies do not hold for strong mutation and show why. We also show that the probability for a random test to experience the problem can be more than 30% and that 50% of the mutants might be affected in a real software system. Finally, we show that there is a similar problem with the theory on sufficient conditional operator replacement.

Small discussion-based courses pose several challenges. Low enrollments make the course difficult to justify and can restrict active discussions. Impromptu discussions are hard to encourage. Students come to class tired, not well prepared, reluctant to speak out, or not able to verbalize abstract thoughts fast enough to fully engage. Sometimes a few students dominate the discussions while other students stay silent. This paper describes a novel teaching model that was created to allow one professor to teach the same course at multiple universities. As the course design emerged, the asynchronous online distributed nature of the course turned out to not only solve the initial problem, but also other challenges of discussion-based courses. Instructors and students found this model led to more engagement, increased learning, and higher performance.

This document reports a technical description of ELVIRA project results obtained as part of Work-package 2.1 entitled “Complex Dependencies Analysis”. In this technical report, we review attempts in recent researches where connections are regarded as influencing factors to  IT systems monitoring critical infrastructure, based on which potential dependencies and resulting disturbances are identified and categorized. Each kind of dependence has been discussed based on our own entity based model. Among those dependencies, logical and functional connections have been analysed with more details on modelling and simulation techniques.

Smart grid employs ICT infrastructure and network connectivity to optimize efficiency and deliver new functionalities. This evolu- tion is associated with an increased risk for cybersecurity threats that may hamper smart grid operations. Power utility providers need tools for assessing risk of prevailing cyberthreats over ICT infrastructures. The need for frameworks to guide the develop- ment of these tools is essential to define and reveal vulnerability analysis indicators. We propose a data-driven approach for design- ing testbeds to evaluate the vulnerability of cyberphysical systems against cyberthreats. The proposed framework uses data reported from multiple components of cyberphysical system architecture layers, including physical, control, and cyber layers. At the phys- ical layer, we consider component inventory and related physi- cal flows. At the control level, we consider control data, such as SCADA data flows in industrial and critical infrastructure control systems. Finally, at the cyber layer level, we consider existing secu- rity and monitoring data from cyber-incident event management tools, which are increasingly embedded into the control fabrics of cyberphysical systems.

Real-time systems have to be verified and tested for timely behavior as well as functional behavior. Thus, time is an extra dimension that adds to the complexity of software testing. A timed automata model with a model-checker can be used to generate timed test traces. To properly test the timely behavior, the set of test traces should challenge the different time constraints in the model. This paper describes and adapts mutation operators that target such time constraints in timed automata models. Time mutation operators apply a delta to the time constraints to help testers design tests that exceed the time constraints. We suggest that the size of this delta determines how easy the mutant is to kill and that the optimal delta varies by the program, mutation operator, and the individual mutant. To avoid trivial and equivalent time mutants, the delta should be set individually for each mutant. We discuss mutant subsumption and define the problem of finding dominator mutants in this new domain. In this position paper, we outline an iterative tuning process where a statistical model-checker, UPPAAL SMC, is used to: (i) create a tuned set of dominator time mutants, and (ii) generate test traces that kill the mutants.

This document reports a technical description of ELVIRA project results obtained as part of Work- package 4.1 entitled “Multi-agent systems for power Grid monitoring”. ELVIRA project is a collaboration between researchers in School of IT at University of Skövde and Combitech Technical Consulting Company in Sweden, with the aim to design, develop and test a testbed simulator for critical infrastructures cybersecurity. This report outlines intelligent approaches that continuously analyze data flows generated by Supervisory Control And Data Acquisition (SCADA) systems, which monitor contemporary power grid infrastructures. However, cybersecurity threats and security mechanisms cannot be analyzed and tested on actual systems, and thus testbed simulators are necessary to assess vulnerabilities and evaluate the infrastructure resilience against cyberattacks. This report suggests an agent-based model to simulate SCADA- like cyber-components behaviour when facing cyber-infection in order to experiment and test intelligent mitigation mechanisms. 

In testing, engineers want to run the most useful tests early (prioritization). When tests are run hundreds or thousands of times, minimizing a test set can result in significant savings (minimization). This paper proposes a new analysis technique to address both the minimal test set and the test case prioritization problems. This paper precisely defines the concept of mutant stubbornness, which is the basis for our analysis technique. We empirically compare our technique with other test case minimization and prioritization techniques in terms of the size of the minimized test sets and how quickly mutants are killed. We used seven C language subjects from the Siemens Repository, specifically the test sets and the killing matrices from a previous study. We used 30 different orders for each set and ran every technique 100 times over each set. Results show that our analysis technique performed significantly better than prior techniques for creating minimal test sets and was able to establish new bounds for all cases. Also, our analysis technique killed mutants as fast or faster than prior techniques. These results indicate that our mutant stubbornness technique constructs test sets that are both minimal in size, and prioritized effectively, as well or better than other techniques.