Biblio

Principles of agile information systems development (ISD) have attracted the interest of practice as well as research. The goal of this literature review is to validate, update and extend previous reviews in terms of the general state of research on agile ISD. Besides including categories such as the employed research methods and data collection techniques, the importance of theory is highlighted by evaluating the theoretical foundations and contributions of former studies. Since agile ISD is rooted in the IS as well as software engineering discipline, important outlets of both disciplines are included in the search process, resulting in 482 investigated papers. The findings show that quantitative studies and the theoretical underpinnings of agile ISD are lacking. Extreme Programming is still the most researched agile ISD method, and more efforts on Scrum are needed. In consequence, multiple research gaps that need further research attention are identified.

While many theoretical and simulation works have highlighted the potential gains of cognitive radio, several technical issues still need to be evaluated from an experimental point of view. Deploying complex heterogeneous system scenarios is tedious, time consuming and hardly reproducible. To address this problem, we have developed a new experimental facility, called CorteXlab, that allows complex multi-node cognitive radio scenarios to be easily deployed and tested by anyone in the world. Our objective is not to design new software defined radio (SDR) nodes, but rather to provide a comprehensive access to a large set of high performance SDR nodes. The CorteXlab facility offers a 167 m2 electromagnetically (EM) shielded room and integrates a set of 24 universal software radio peripherals (USRPs) from National Instruments, 18 PicoSDR nodes from Nutaq and 42 IoT-Lab wireless sensor nodes from Hikob. CorteXlab is built upon the foundations of the SensLAB testbed and is based the free and open-source toolkit GNU Radio. Automation in scenario deployment, experiment start, stop and results collection is performed by an experiment controller, called Minus. CorteXlab is in its final stages of development and is already capable of running test scenarios. In this contribution, we show that CorteXlab is able to easily cope with the usual issues faced by other testbeds providing a reproducible experiment environment for CR experimentation.
 

Threats to modern ICT systems are rapidly changing these days. Organizations are not mainly concerned about virus infestation, but increasingly need to deal with targeted attacks. This kind of attacks are specifically designed to stay below the radar of standard ICT security systems. As a consequence, vendors have begun to ship self-learning intrusion detection systems with sophisticated heuristic detection engines. While these approaches are promising to relax the serious security situation, one of the main challenges is the proper evaluation of such systems under realistic conditions during development and before roll-out. Especially the wide variety of configuration settings makes it hard to find the optimal setup for a specific infrastructure. However, extensive testing in a live environment is not only cumbersome but usually also impacts daily business. In this paper, we therefore introduce an approach of an evaluation setup that consists of virtual components, which imitate real systems and human user interactions as close as possible to produce system events, network flows and logging data of complex ICT service environments. This data is a key prerequisite for the evaluation of modern intrusion detection and prevention systems. With these generated data sets, a system's detection performance can be accurately rated and tuned for very specific settings.

Concurrent programs are prone to various classes of difficult-to-detect faults, of which data races are particularly prevalent. Prior work has attempted to increase the cost-effectiveness of approaches for testing for data races by employing race detection techniques, but to date, no work has considered cost-effective approaches for re-testing for races as programs evolve. In this paper we present SimRT, an automated regression testing framework for use in detecting races introduced by code modifications. SimRT employs a regression test selection technique, focused on sets of program elements related to race detection, to reduce the number of test cases that must be run on a changed program to detect races that occur due to code modifications, and it employs a test case prioritization technique to improve the rate at which such races are detected. Our empirical study of SimRT reveals that it is more efficient and effective for revealing races than other approaches, and that its constituent test selection and prioritization components each contribute to its performance.

Dynamic taint analysis and forward symbolic execution are quickly becoming staple techniques in security analyses. Example applications of dynamic taint analysis and forward symbolic execution include malware analysis, input filter generation, test case generation, and vulnerability discovery. Despite the widespread usage of these two techniques, there has been little effort to formally define the algorithms and summarize the critical issues that arise when these techniques are used in typical security contexts. The contributions of this paper are two-fold. First, we precisely describe the algorithms for dynamic taint analysis and forward symbolic execution as extensions to the run-time semantics of a general language. Second, we highlight important implementation choices, common pitfalls, and considerations when using these techniques in a security context.

This paper examines security faults/vulnerabilities reported for Fedora. Results indicate that, at least in some situations, fault roughly constant may be used to guide estimation of residual vulnerabilities in an already released product, as well as possibly guide testing of the next version of the product.

Detecting and preventing attacks before they compromise a system can be done using acceptance testing, redundancy based mechanisms, and using external consistency checking such external monitoring and watchdog processes. Diversity-based adjudication, is a step towards an oracle that uses knowable behavior of a healthy system. That approach, under best circumstances, is able to detect even zero-day attacks. In this approach we use functionally equivalent but in some way diverse components and we compare their output vectors and reactions for a given input vector. This paper discusses practical relevance of this approach in the context of recent web-service attacks.