Exploratory Test Agents for Stateful Software Systems

The adequate testing of stateful software systems is a hard and costly activity. Failures that result from complex stateful interactions can be of high impact, and it can be hard to replicate failures resulting from erroneous stateful interactions. Addressing this problem in an automatic way would save cost and time and increase the quality of software systems in the industry. In this paper, we propose an approach that uses agents to explore software systems with the intention to find faults and gain knowledge

Building Decision Adviser Bots

This overview article explores the prospects and promises of new technologies for developing conversational software to aid, assist and advise people in personal and organizational decision situations. The quest for conversational decision advisers began in the 1970s with the development of interactive, computing systems like the Hewlett-Packard 2000 Access Time- Share systems. With the advent of Cloud-based, Artificial Intelligence development environments, the capabilities needed to develop conversational software are increasingly available and easy to use. Hence, it is feasible to develop decision adviser (DA) bots and the bots are easier to deploy. Bots can be built for action taking and for question and answer dialogs. DA bots can be deployed for use in both structured and semi-structured decision situations. DA bots can perform increasingly complex tasks. Overall, more exploratory design science research is needed to improve our understanding of the design, development, and deployment of DA bots for use by managers, customers, and clients

Logic programming in the context of multiparadigm programming: the Oz experience

Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This article has two goals: to give a tutorial of logic programming in Oz and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We conclude with lessons learned from this work, a brief history of Oz, and many entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic Programming

An overview of S-OGSA: A Reference Semantic Grid Architecture

The Grid's vision, of sharing diverse resources in a flexible, coordinated and secure manner through dynamic formation and disbanding of virtual communities, strongly depends on metadata. Currently, Grid metadata is generated and used in an ad hoc fashion, much of it buried in the Grid middleware's code libraries and database schemas. This ad hoc expression and use of metadata causes chronic dependency on human intervention during the operation of Grid machinery, leading to systems which are brittle when faced with frequent syntactic changes in resource coordination and sharing protocols. The Semantic Grid is an extension of the Grid in which rich resource metadata is exposed and handled explicitly, and shared and managed via Grid protocols. The layering of an explicit semantic infrastructure over the Grid Infrastructure potentially leads to increased interoperability and greater flexibility. In recent years, several projects have embraced the Semantic Grid vision. However, the Semantic Grid lacks a Reference Architecture or any kind of systematic framework for designing Semantic Grid components or applications. The Open Grid Service Architecture ( OGSA) aims to define a core set of capabilities and behaviours for Grid systems. We propose a Reference Architecture that extends OGSA to support the explicit handling of semantics, and defines the associated knowledge services to support a spectrum of service capabilities. Guided by a set of design principles, Semantic-OGSA ( S-OGSA) defines a model, the capabilities and the mechanisms for the Semantic Grid. We conclude by highlighting the commonalities and differences that the proposed architecture has with respect to other Grid frameworks. (c) 2006 Elsevier B. V. All rights reserved

LUNA: A Model-Based Universal Analysis Framework for Large Language Models

Over the past decade, Artificial Intelligence (AI) has had great success recently and is being used in a wide range of academic and industrial fields. More recently, LLMs have made rapid advancements that have propelled AI to a new level, enabling even more diverse applications and industrial domains with intelligence, particularly in areas like software engineering and natural language processing. Nevertheless, a number of emerging trustworthiness concerns and issues exhibited in LLMs have already recently received much attention, without properly solving which the widespread adoption of LLMs could be greatly hindered in practice. The distinctive characteristics of LLMs, such as the self-attention mechanism, extremely large model scale, and autoregressive generation schema, differ from classic AI software based on CNNs and RNNs and present new challenges for quality analysis. Up to the present, it still lacks universal and systematic analysis techniques for LLMs despite the urgent industrial demand. Towards bridging this gap, we initiate an early exploratory study and propose a universal analysis framework for LLMs, LUNA, designed to be general and extensible, to enable versatile analysis of LLMs from multiple quality perspectives in a human-interpretable manner. In particular, we first leverage the data from desired trustworthiness perspectives to construct an abstract model as an auxiliary analysis asset, which is empowered by various abstract model construction methods. To assess the quality of the abstract model, we collect and define a number of evaluation metrics, aiming at both abstract model level and the semantics level. Then, the semantics, which is the degree of satisfaction of the LLM w.r.t. the trustworthiness perspective, is bound to and enriches the abstract model with semantics, which enables more detailed analysis applications for diverse purposes.Comment: 44 pages, 9 figure

A Search-Based Testing Approach for Deep Reinforcement Learning Agents

Deep Reinforcement Learning (DRL) algorithms have been increasingly employed during the last decade to solve various decision-making problems such as autonomous driving and robotics. However, these algorithms have faced great challenges when deployed in safety-critical environments since they often exhibit erroneous behaviors that can lead to potentially critical errors. One way to assess the safety of DRL agents is to test them to detect possible faults leading to critical failures during their execution. This raises the question of how we can efficiently test DRL policies to ensure their correctness and adherence to safety requirements. Most existing works on testing DRL agents use adversarial attacks that perturb states or actions of the agent. However, such attacks often lead to unrealistic states of the environment. Their main goal is to test the robustness of DRL agents rather than testing the compliance of agents' policies with respect to requirements. Due to the huge state space of DRL environments, the high cost of test execution, and the black-box nature of DRL algorithms, the exhaustive testing of DRL agents is impossible. In this paper, we propose a Search-based Testing Approach of Reinforcement Learning Agents (STARLA) to test the policy of a DRL agent by effectively searching for failing executions of the agent within a limited testing budget. We use machine learning models and a dedicated genetic algorithm to narrow the search towards faulty episodes. We apply STARLA on Deep-Q-Learning agents which are widely used as benchmarks and show that it significantly outperforms Random Testing by detecting more faults related to the agent's policy. We also investigate how to extract rules that characterize faulty episodes of the DRL agent using our search results. Such rules can be used to understand the conditions under which the agent fails and thus assess its deployment risks

Network Intrusion Detection and Prevention Systems in Educational Systems : A case of Yaba College of Technology

Nwogu, Emeka Joshua. 2012. Network Intrusion Detection and Prevention Systems in Educational Systems - A case of Yaba College of Technology. Bachelor’s Thesis. Kemi-Tornio University of Applied Sciences. Business and Culture. Pages 66. Appendix 1. The objective of this thesis work is to put forward a solution for improving the security network of Yaba College of Technology (YCT). This work focuses on implementation of a network intrusion detection and prevention system (IDPS), due to constant intrusions on the YCT’s network. Various networks attacks and their mitigation techniques are also discussed, to give a clear picture of intrusions. The work will help the College’s administrators to become increasingly cautions of attacks and perform regular risk analyses. The research methodologies used in this work are descriptive and exploratory research. In addition, a questionnaire survey and interviews were used to collect data necessary for in-depth knowledge of the intrusions in the College. The choice of the research methods was found relevant for the current work. Furthermore, the researcher intended to gain an increased understanding of and provide a detailed picture of IDPS and the issues to consider when implementing the system. Network intrusion has been a security issue since the inception of the computer systems and the Internet. When breaking into a computer or network system, confidentiality, integrity and availability (CIA) are the three most aspect of security that are targets for intruders. The CIA, important aspects of security, and other network resources, need to be well protected using robust security devices. Based on the research tests and results, this thesis proposes implementation of IDPS on the College’s network, which is an essential aspect of securing information and network resources

Tilt and Multitouch Input for Tablet Play of Real-Time Strategy Games

We are studying the use of tilt-enabled handheld touchscreen devices as an interface for top-down strategy games. We will explore how using different input modes (tilt and touch) compare for certain tasks in terms of efficiency and comfort. Real-time and turn-based strategy games are a popular form of electronic gaming, though these games currently have only minor representation on tablets. This genre of game requires both a wide variety of input and the display of a wealth of information. We are exploring whether, with suitable interface developments, this genre can become as accessible on tablet devices as on traditional computers. These interface approaches may also prove useful for expanding the presence of other game genres in the mobile space