Analyzing execution traces: critical-path analysis and distance analysis

International Journal on Software Tools for Technology Transfer - System designers make trade-offs between metrics of interest such as execution time, functional quality and cost to create a...     

An innovative <Emphasis Type="BoldItalic">H</Emphasis><Subscript><Emphasis Type="BoldItalic">∞</Emphasis></Subscript>–norm based worst case scenario approach for dynamic compliance optimization with applications to viscoelastic beams

A novel frequency–based definition of dynamic compliance is introduced within the framework of H _∞ –norm based structural dynamics in the presence of load uncertainties. The system itself is supposed to depend on a vector of design parameters with respect to which an optimal design is pursued. A three-step worst-case-scenario is then developed that finds the minimum-compliance structure capable of accounting for the entire norm–bounded load sets. Once the problem is initialized, the current worst load is found that is used as input to the minimization of the structural compliance and the procedure is repeated until convergence. Numerical examples are eventually proposed that deal with viscoelastic beams discretized via a truly–mixed finite–element scheme.     

Designing vulnerability testing tools for web services: approach,components, and tools

This paper proposes a generic approach for designing vulnerability testing tools for web services, which includes the definition of the testing procedure and the tool components. Based on the proposed approach, we present the design of three innovative testing tools that implement three complementary techniques (improved penetration testing, attack signatures and interface monitoring, and runtime anomaly detection) for detecting injection vulnerabilities, thus offering an extensive support for different scenarios. A case study has been designed to demonstrate the tools for the particular case of SQL Injection vulnerabilities. The experimental evaluation demonstrates that the tools can effectively be used in different scenarios and that they outperform well-known commercial tools by achieving higher detection coverage and lower false-positive rates.     

On improving resistance to Denial of Service and key provisioning scalability of the DTLS handshake

DTLS is a transport layer security protocol designed to provide secure communication over unreliable datagram protocols. Before starting to communicate, a DTLS client and server perform a specific handshake in order to establish a secure session and agree on a common security context. However, the DTLS handshake is affected by two relevant issues. First, the DTLS server is vulnerable to a specific Denial of Service (DoS) attack aimed at forcing the establishment of several half-open sessions. This may exhaust memory and network resources on the server, so making it less responsive or even unavailable to legitimate clients. Second, although it is one of the most efficient key provisioning approaches adopted in DTLS, the pre-shared key provisioning mode does not scale well with the number of clients, it may result in scalability issues on the server side, and it complicates key re-provisioning in dynamic scenarios. This paper presents a single and efficient security architecture which addresses both issues, by substantially limiting the impact of DoS, and reducing the number of keys stored on the server side to one unit only. Our approach does not break the existing standard and does not require any additional message exchange between DTLS client and server. Our experimental results show that our approach requires a shorter amount of time to complete a handshake execution and consistently reduces the time a DTLS server is exposed to a DoS instance. We also show that it considerably improves a DTLS server in terms of service availability and robustness against DoS attack.     

The shape of feature code: an analysis of twenty C-preprocessor-based systems

Feature annotations (e.g., code fragments guarded by #ifdef C-preprocessor directives) control code extensions related to features. Feature annotations have long been said to be undesirable. When maintaining features that control many annotations, there is a high risk of ripple effects. Also, excessive use of feature annotations leads to code clutter, hinder program comprehension and harden maintenance. To prevent such problems, developers should monitor the use of feature annotations, for example, by setting acceptable thresholds. Interestingly, little is known about how to extract thresholds in practice, and which values are representative for feature-related metrics. To address this issue, we analyze the statistical distribution of three feature-related metrics collected from a corpus of 20 well-known and long-lived C-preprocessor-based systems from different domains. We consider three metrics: scattering degree of feature constants, tangling degree of feature expressions, and nesting depth of preprocessor annotations. Our findings show that feature scattering is highly skewed; in 14 systems (70 %), the scattering distributions match a power law, making averages and standard deviations unreliable limits. Regarding tangling and nesting, the values tend to follow a uniform distribution; although outliers exist, they have little impact on the mean, suggesting that central statistics measures are reliable thresholds for tangling and nesting. Following our findings, we then propose thresholds from our benchmark data, as a basis for further investigations.     

A participative end-user method for multi-perspective business process elicitation and improvement

A business process can be characterized by multiple perspectives (intentional, organizational, operational, functional, interactional, informational, etc). Business process modeling must allow different stakeholders to analyze and represent process models according to these different perspectives. This representation is traditionally built using classical data acquisition methods together with a process representation language such as BPMN or UML. These techniques and specialized languages can easily become hard, complex and time consuming. In this paper, we propose ISEA, a participative end-user modeling approach that allows the stakeholders in a business process to collaborate together in a simple way to communicate and improve the business process elicitation in an accurate and understandable manner. Our approach covers the organizational perspective of business processes, exploits the information compiled during the elicitation of the organizational perspective and touches lightly an interactional perspective allowing users to create customized interface sketches to test the user interface navigability and the coherence within the processes. Thus, ISEA can be seen as a participative end-user modeling approach for business process elicitation and improvement.     

Probabilistic knowledge representation using the principle of maximum entropy and Gröbner basis theory

An often used methodology for reasoning with probabilistic conditional knowledge bases is provided by the principle of maximum entropy (so-called MaxEnt principle) that realises an idea of least amount of assumed information and thus of being as unbiased as possible. In this paper we exploit the fact that MaxEnt distributions can be computed by solving nonlinear equation systems that reflect the conditional logical structure of these distributions. We apply the theory of Gröbner bases that is well known from computational algebra to the polynomial system which is associated with a MaxEnt distribution, in order to obtain results for reasoning with maximum entropy. We develop a three-phase compilation scheme extracting from a knowledge base consisting of probabilistic conditionals the information which is crucial for MaxEnt reasoning and transforming it to a Gröbner basis. Based on this transformation, a necessary condition for knowledge bases to be consistent is derived. Furthermore, approaches to answering MaxEnt queries are presented by demonstrating how inferring the MaxEnt probability of a single conditional from a given knowledge base is possible. Finally, we discuss computational methods to establish general MaxEnt inference rules.     

Position USBL/DVL sensor-based navigation filter in the presence of unknown ocean currents

This paper presents a novel approach to the design of globally asymptotically stable (GAS) position and velocity filters for Autonomous Underwater Vehicles (AUVs) based directly on the sensor readings of an Ultra-short Baseline (USBL) acoustic array system and a Doppler Velocity Log (DVL). The proposed methodology is based on an equivalent linear time-varying (LTV) system that fully captures the dynamics of the nonlinear system, allowing for the use of powerful linear system analysis and filtering design tools that yield GAS filter error dynamics. Numerical results using Monte Carlo simulations and comparison to the Bayesian Cramér Rao Bound (BCRB) reveal that the performance of the proposed filter is tight to this theoretical estimation error lower bound. In comparison with other approaches, the present solution achieves the same level of performance of the Extended Kalman Filter (EKF), which does not offer GAS guarantees, and outperforms other classical filtering approaches designed in inertial coordinates instead of the body-fixed coordinate frame.     

Ant algorithms and stigmergy

Ant colonies, and more generally social insect societies, are distributed systems that, in spite of the simplicity of their individuals, present a highly structured social organization. As a result of this organization, ant colonies can accomplish complex tasks that in some cases far exceed the individual capacities of a single ant. The study of ant colonies behavior and of their self-organizing capacities is interesting for computer scientists because it provides models of distributed organization which are useful to solve difficult optimization and distributed control problems. In this paper we overview some models derived from the observation of real ants, emphasizing the role played by stigmergy as distributed communication paradigm, and we show how these models have inspired a number of novel algorithms for the solution of distributed optimization and distributed control problems.     

Fault detection and isolation in robotic manipulators via neural networks: A comparison among three architectures for residual analysis

In this article we discuss artificial neural networks‐based fault detection and isolation (FDI) applications for robotic manipulators. The artificial neural networks (ANNs) are used for both residual generation and residual analysis. A multilayer perceptron (MLP) is employed to reproduce the dynamics of the robotic manipulator. Its outputs are compared with actual position and velocity measurements, generating the so‐called residual vector. The residuals, when properly analyzed, provides an indication of the status of the robot (normal or faulty operation). Three ANNs architectures are employed in the residual analysis. The first is a radial basis function network (RBFN) which uses the residuals of position and velocity to perform fault identification. The second is again an RBFN, except that it uses only the velocity residuals. The third is an MLP which also performs fault identification utilizing only the velocity residuals. The MLP is trained with the classical back‐propagation algorithm and the RBFN is trained with a Kohonen self‐organizing map (KSOM). We validate the concepts discussed in a thorough simulation study of a Puma 560 and with experimental results with a 3‐joint planar manipulator. © 2001 John Wiley & Sons, Inc.