Reactive Rules for Emergency Management

The goal of the following survey on Event-Condition-Action (ECA) Rules is to come to a common understanding and intuition on this topic within EMILI. Thus it does not give an academic overview on Event-Condition-Action Rules which would be valuable for computer scientists only. Instead the survey tries to introduce Event-Condition-Action Rules and their use for emergency management based on real-life examples from the use-cases identified in Deliverable 3.1. In this way we hope to address both, computer scientists and security experts, by showing how the Event-Condition-Action Rule technology can help to solve security issues in emergency management. The survey incorporates information from other work packages, particularly from Deliverable D3.1 and its Annexes, D4.1, D2.1 and D6.2 wherever possible

Retroduction, set-theoretic configurational approaches and generative mechanisms: some preliminary insights

Retroduction is a thought operation that has been investigated in a limited fashion in Information Systems (IS) research. Yet, it has the potential of reframing IS research because it can shed a new light on the study of causal mechanisms. In this paper, we call for a renewed effort in the use of retroduction in the study of IS phenomena. Specifically, we claim that IS researchers could retroduce causal mechanisms by leveraging Qualitative Comparative Analysis (QCA) counterfactual approach to causation. Preliminary insights are discussed

Modal Kleene algebra and applications - a survey

Modal Kleene algebras are Kleene algebras with forward and backward modal operators defined via domain and codomain operations. They provide a concise and convenient algebraic framework that subsumes various other calculi and allows treating quite a variety of areas. We survey the basic theory and some prominent applications. These include, on the system semantics side, Hoare logic and PDL (Propositional Dynamic Logic), wp calculus and predicate transformer semantics, temporal logics and termination analysis of rewrite and state transition systems. On the derivation side we apply the framework to game analysis and greedy-like algorithms

A two-level Product Recommender for E-commerce Sites by Using Sequential Pattern Analysis

With the development of communication networks and rapid growth of their applications, huge amount of information have been produced. Major part of these information are in electronic stores, and hence it's really hard to find desired products inside huggermugger. Product Recommendation System (PRS) tries to solve this problem by giving appropriate and fast recommendations to the customers. This paper proposes a two-level product recommender for E-commerce sites. At first, the available products are clustered by using C-Means algorithm to create groups of products with similar characteristics. Then, the second level considers the customers’ behavior and their purchase history for drawing the relationships between products by using Sequential Pattern Analysis (SPA) method. These relationships, eventually, will lead to appropriate recommendation for customers and also increases the likelihood of selling related products in electronic transactions. Extensive numerical simulations over UCI transactions 10k dataset indicates that 87% of records in mined sequential patterns are predicted correctly and the accuracy of recommendations is more than other RPSs

Necessary and sufficient factors in employee downsizing? A qualitative comparative analysis of lay-offs in France and the UK, 2008-2013

Embedded in the literature on financialization and institutional approaches, this study is an examination of the causal factors of employee downsizing in two institutionally dissimilar settings, France and the UK, using the fuzzy sets variant of Qualitative Comparative Analysis. The findings show that the roughly equivalent use of large-scale lay-offs in the two countries is coupled with strikingly different causal factors. Our argument suggests the importance of complex causation whereby employee downsizing reflects the growing influence of financial considerations in the governance of companies, but its diffusion across countries is shaped by different configurations of institutional arrangements

Using Machine Learning to Generate Test Oracles: A Systematic Literature Review

Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle types, researcher goals, the ML techniques applied, how the generation process was assessed, and the open research challenges in this emerging field.Based on a sample of 22 relevant studies, we observed that ML algorithms generated test verdict, metamorphic relation, and - most commonly - expected output oracles. Almost all studies employ a supervised or semi-supervised approach, trained on labeled system executions or code metadata - including neural networks, support vector machines, adaptive boosting, and decision trees. Oracles are evaluated using the mutation score, correct classifications, accuracy, and ROC. Work-to-date show great promise, but there are significant open challenges regarding the requirements imposed on training data, the complexity of modeled functions, the ML algorithms employed - and how they are applied - the benchmarks used by researchers, and replicability of the studies. We hope that our findings will serve as a roadmap and inspiration for researchers in this field

Using Machine Learning to Generate Test Oracles: A Systematic Literature Review

Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle types, researcher goals, the ML techniques applied, how the generation process was assessed, and the open research challenges in this emerging field. Based on a sample of 22 relevant studies, we observed that ML algorithms generated test verdict, metamorphic relation, and - most commonly - expected output oracles. Almost all studies employ a supervised or semi-supervised approach, trained on labeled system executions or code metadata - including neural networks, support vector machines, adaptive boosting, and decision trees. Oracles are evaluated using the mutation score, correct classifications, accuracy, and ROC. Work-to-date show great promise, but there are significant open challenges regarding the requirements imposed on training data, the complexity of modeled functions, the ML algorithms employed - and how they are applied - the benchmarks used by researchers, and replicability of the studies. We hope that our findings will serve as a roadmap and inspiration for researchers in this field.Comment: Pre-print. Article accepted to 1st International Workshop on Test Oracles at ESEC/FSE 202