Hany H. Ammar

A scenario-based reliability analysis approach for component-based software

This paper introduces a reliability model, and a reliability analysis technique for component-based software. The technique is named Scenario-Based Reliability Analysis (SBRA). Using scenarios of component interactions, we construct a probabilistic model named Component-Dependency Graph (CDG). Based on CDG, a reliability analysis algorithm is developed to analyze the reliability of the system as a function of reliabilities of its architectural constituents. An extension of the proposed model and algorithm is also developed for distributed software systems. The proposed approach has the following benefits: 1) It is used to analyze the impact of variations and uncertainties in the reliability of individual components, subsystems, and links between components on the overall reliability estimate of the software system. This is particularly useful when the system is built partially or fully from existing off-the-shelf components; 2) It is suitable for analyzing the reliability of distributed software systems because it incorporates link and delivery channel reliabilities; 3) The technique is used to identify critical components, interfaces, and subsystems; and to investigate the sensitivity of the application reliability to these elements; 4) The approach is applicable early in the development lifecycle, at the architecture level. Early detection of critical architecture elements, those that affect the overall reliability of the system the most, is useful in delegating resources in later development phases.

Architectural-level risk analysis using UML

Risk assessment is an essential part in managing software development. Performing risk assessment during the early development phases enhances resource allocation decisions. In order to improve the software development process and the quality of software products, we need to be able to build risk analysis models based on data that can be collected early in the development process. These models will help identify the high-risk components and connectors of the product architecture, so that remedial actions may be taken in order to control and optimize the development process and improve the quality of the product. In this paper, we present a risk assessment methodology which can be used in the early phases of the software life cycle. We use the Unified Modeling Language (UML) and commercial modeling environment Rational Rose Real Time (RoseRT) to obtain UML model statistics. First, for each component and connector in software architecture, a dynamic heuristic risk factor is obtained and severity is assessed based on hazard analysis. Then, a Markov model is constructed to obtain scenarios risk factors. The risk factors of use cases and the overall system risk factor are estimated using the scenarios risk factors. Within our methodology, we also identify critical components and connectors that would require careful analysis, design, implementation, and more testing effort. The risk assessment methodology is applied on a pacemaker case study.

A methodology for architecture-level reliability risk analysis

The paper presents a methodology for reliability risk assessment at the early stages of the development lifecycle, namely, the architecture level. We describe a heuristic risk assessment methodology that is based on dynamic metrics. The methodology uses dynamic complexity and dynamic coupling metrics to define complexity factors for the architecture elements (components and connectors). Severity analysis is performed using Failure Mode and Effect Analysis (FMEA) as applied to architecture models. We combine severity and complexity factors to develop heuristic risk factors for the architecture components and connectors. Based on analysis scenarios, we develop a risk assessment model that represents components, connectors, component risk factors, connector risk factors, and probabilities of component interactions. We also develop a risk analysis algorithm that aggregates risk factors of components and connectors to the architectural level. Using the risk aggregation and the risk analysis model, we show how to analyze the overall risk factor of the architecture as the function of the risk factors of its constituting components and connectors. A case study of a pacemaker architecture is used to illustrate the application of the methodology. The methodology is used to identify critical components and connectors and to investigate the sensitivity of the architecture risk factor to changes in the heuristic risk factors of the architecture elements.

Dynamic metrics for object oriented designs

As object-oriented (OO) analysis and design techniques become more widely used, the demand on assessing the quality of OO designs increases substantially. Recently, there has been much research effort devoted to developing and empirically validating metrics for OO design quality. Complexity, coupling, and cohesion have received a considerable interest in the field. Despite the rich body of research and practice in developing design quality metrics, there has been less emphasis on dynamic metrics for OO designs. The complex dynamic behavior of many real-time applications motivates a shift in interest from traditional static metrics to dynamic metrics. This paper addresses the problem of measuring the quality of OO designs using dynamic metrics. We present a metrics suite to measure the quality of designs at an early development phase. The suite consists of metrics for dynamic complexity and object coupling based on execution scenarios. The proposed measures are obtained from executable design models. We apply the dynamic metrics to assess the quality of a pacemaker application. Results from the case study are used to compare static metrics to the proposed dynamic metrics and hence identify the need for empirical studies to explore the dependency of design quality on each.

On the value of user preferences in search-based software engineering: a case study in software product lines

Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature maps) using various search-based software engineering methods. As we increase the number of optimization objectives, we find that methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0% violations of domain constraints. Our conclusion is that we need to change our methods for search-based software engineering, particularly when studying complex decision spaces.

Teeth segmentation in digitized dental X-ray films using mathematical morphology

Automating the process of postmortem identification of individuals using dental records is receiving increased attention. Teeth segmentation from dental radiographic films is an essential step for achieving highly automated postmortem identification. In this paper, we offer a mathematical morphology approach to the problem of teeth segmentation. We also propose a grayscale contrast stretching transformation to improve the performance of teeth segmentation. We compare and contrast our approach with other approaches proposed in the literature based on a theoretical and empirical basis. The results show that in addition to its capability of handling bitewing and periapical dental radiographic views, our approach exhibits the lowest failure rate among all approaches studied.

Time scale decomposition of a class of generalized stochastic Petri net models

A time-scale decomposition (TSD) algorithm of a class of generalized stochastic Petri net (GSPN) models of systems comprising activities whose duration differ by orders of magnitude is presented. The GSPN model of a system can be decomposed into a hierarchical sequence of aggregated subnets, each of which is valid at a certain time scale. These smaller subnets are solved in isolation and their solutions are combined to get the solution of the whole system. A degradable multiprocessor system which would be intractable using conventional techniques, is analyzed using TSD. The complexity of the TSD algorithm can be orders of magnitude smaller without any significant loss in the accuracy of the result. In general, the error due to aggregation is proportional to the maximum degree of coupling between aggregates. An expression of the error due to aggregation is also given in terms of the ratio of fast and slow transitions in the GSPN model. The algorithm is easy to use and can be easily automated. >

Error propagation in software architectures

The study of software architectures is emerging as an important discipline in software engineering, due to its emphasis on large scale composition of software products, and its support for emerging software engineering paradigms such as product line engineering, component based software engineering, and software evolution. Architectural attributes differ from code-level software attributes in that they focus on the level of components and connectors, and that they are meaningful for an architecture. In this paper, we focus on a specific architectural attribute, which is the error propagation probability throughout the architecture, i.e. the probability that an error that arises in one component propagates to other components. We introduce, analyze, and validate formulas for estimating these probabilities using architectural level information.

Scalable product line configuration: a straw to break the camel's back

Software product lines are hard to configure. Techniques that work for medium sized product lines fail for much larger product lines such as the Linux kernel with 6000+ features. This paper presents simple heuristics that help the Indicator-Based Evolutionary Algorithm (IBEA) in finding sound and optimum configurations of very large variability models in the presence of competing objectives. We employ a combination of static and evolutionary learning of model structure, in addition to utilizing a pre-computed solution used as a “seed” in the midst of a randomly-generated initial population. The seed solution works like a single straw that is enough to break the camels back -given that it is a feature-rich seed. We show promising results where we can find 30 sound solutions for configuring upward of 6000 features within 30 minutes.

Model-based performance risk analysis

Performance is a nonfunctional software attribute that plays a crucial role in wide application domains spreading from safety-critical systems to e-commerce applications. Software risk can be quantified as a combination of the probability that a software system may fail and the severity of the damages caused by the failure. In this paper, we devise a methodology for estimation of performance-based risk factor, which originates from violations, of performance requirements, (namely, performance failures). The methodology elaborates annotated UML diagrams to estimate the performance failure probability and combines it with the failure severity estimate which is obtained using the functional failure analysis. We are thus able to determine risky scenarios as well as risky software components, and the analysis feedback can be used to improve the software design. We illustrate the methodology on an e-commerce case study using step-by step approach, and then provide a brief description of a case study based on large real system.