Data Mining Practical Machine Learning Tools and Techniques

https://hal-supelec.archives-ouvertes.fr/hal-00610053/document

Reliability engineering: Old problems and new challenges

https://ris.utwente.nl/ws/files/13291952/FTA_overview.pdf

Fault tree analysis

Fault tree analysis (FTA) is a very prominent method to analyze the risks related to safety and economically critical assets, like power plants, airplanes, data centers and web shops. FTA methods comprise of a wide variety of modelling and analysis techniques, supported by a wide range of software tools. This paper surveys over 150 papers on fault tree analysis, providing an in-depth overview of the state-of-the-art in FTA. Concretely, we review standard fault trees, as well as extensions such as dynamic FT, repairable FT, and extended FT. For these models, we review both qualitative analysis methods, like cut sets and common cause failures, and quantitative techniques, including a wide variety of stochastic methods to compute failure probabilities. Numerous examples illustrate the various approaches, and tables present a quick overview of results.

Fault Tree Analysis : A survey of the state-of-the-art in modeling, analysis and tools

Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches.

https://www.cse.cuhk.edu.hk/lyu/_media/paper/icse0061.pdf?id=home&cache=cache

Collaborative reliability prediction of service-oriented systems

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.

/pdf/architectural-level-risk-analysis-using-uml-5gpic3do4t.pdf

Architectural-level risk analysis using UML

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.

Error propagation in software architectures

Component based development is gaining popularity in the software engineering community. The reliability of components affects the reliability of the system. Different models and theories have been developed to estimate system reliability given the information about system architecture and the quality of the components. Almost always in these models a key attribute of component-based systems, the error propagation between the components, is overlooked and not taken into account in the reliability prediction. We extend our previous work on Bayesian reliability prediction of component based systems by introducing the error propagation probability into the model. We demonstrate the impact of the error propagation in a case study of an automated personnel access control system. We conclude that error propagation may have a significant impact on the system reliability prediction and, therefore, future architecture-based models should not ignore it

Error propagation in the reliability analysis of component based systems

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.

/pdf/model-based-performance-risk-analysis-32xlvihr2u.pdf

Model-based performance risk analysis

: This paper surveys the application of artificial intelligence approaches to the software engineering processes. These approaches can have a major impact on reducing the time to market and improving the quality of software systems in general. Existing survey papers are driven by the AI techniques used, or are focused on specific software engineering processes. This paper relates AI techniques to software engineering processes specified by the IEEE 12207 standard of software engineering. The paper is driven by the activities and tasks specified in the standard for each software engineering process. The paper brings the state of the art of AI techniques closer to the software engineer, and highlights the open research problems for the research community. Keywords: Automated Software Engineering, Artificial Intelligence Techniques. 1. Introduction 2. The software intensive systems we develop these days are becoming much more complex in terms of the number of functional and nonfunctional requirements they need to support. The impact of low quality can also have a catastrophic impact on the mission of these systems in many critical applications. Moreover, the cost of software development dominates the total cost of such systems. Research in applying artificial intelligence techniques to software Engineering have grown tremendously in the last two decades producing a large number of projects and publications. A number of conferences and journals are dedicated to publish the research in this field. The AI techniques are proposed in order to reduce the time to market and enhance the quality of software systems. Yet many of these AI techniques remain largely used by the research community and with little impact on the processes and tools used by the practicing software engineer. The recent survey papers published in this field are mainly targeted to the research community. They are driven by the specific AI techniques used rather than the software engineering activities supported. They are also focused on a specific software engineering process such as software design [28] This survey paper attempts to close the gap between the research and practice of applying AI techniques to the software engineering processes. It also highlights open practical problems to the research community in applying such techniques by surveying the recently proposed work in this area. We use the terminology and the processes defined by the IEEE 12207 standard of software engineering. We then map the current state art of AI art techniques proposed in the literature to specific tasks and activities of some of the software processes define by the 12207 standard. These AI techniques attempt to automate or semi-automate these tasks and produce optimal or semi-optimal solutions in much less time. . The paper is organized as follows. In section 2, we give an overview of the IEEE 12207 standard of software engineering, and describe the most important AI techniques. We survey the current AI techniques proposed for the primary processes of development in sections 3. We highlight the open problems in section 4.

W. Abdelmoez

Papers

Architectural-level risk analysis using UML

Error propagation in software architectures

Error propagation in the reliability analysis of component based systems

Model-based performance risk analysis

Software Engineering Using Artificial Intelligence Techniques: Current State and Open Problems