Reliability Analysis of Complex NASA Systems with Model-Based Engineering

The emergence of model-based engineering, with Model- Based Systems Engineering (MBSE) leading the way, is transforming design and analysis methodologies. The recognized benefits to systems development include moving from document-centric information systems and document-centric project communication to a model-centric environment in which control of design changes in the life cycles is facilitated. In addition, a single source of truth about the system, that is up-to-date in all respects of the design, becomes the authoritative source of data and information about the system. This promotes consistency and efficiency in regard to integration of the system elements as the design emerges and thereby may further optimize the design. Therefore Reliability Engineers (REs) supporting NASA missions must be integrated into model-based engineering to ensure the outputs of their analyses are relevant and value-needed to the design, development, and operational processes for failure risks assessment and communication

Analyzing Clone Evolution for Identifying the Important Clones for Management

Code clones (identical or similar code fragments in a code-base) have dual but contradictory impacts (i.e., both positive and negative impacts) on the evolution and maintenance of a software system. Because of the negative impacts (such as high change-proneness, bug-proneness, and unintentional inconsistencies), software researchers consider code clones to be the number one bad-smell in a code-base. Existing studies on clone management suggest managing code clones through refactoring and tracking. However, a software system's code-base may contain a huge number of code clones, and it is impractical to consider all these clones for refactoring or tracking. In these circumstances, it is essential to identify code clones that can be considered particularly important for refactoring and tracking. However, no existing study has investigated this matter. We conduct our research emphasizing this matter, and perform five studies on identifying important clones by analyzing clone evolution history. In our first study we detect evolutionary coupling of code clones by automatically investigating clone evolution history from thousands of commits of software systems downloaded from on-line SVN repositories. By analyzing evolutionary coupling of code clones we identify a particular clone change pattern, Similarity Preserving Change Pattern (SPCP), such that code clones that evolve following this pattern should be considered important for refactoring. We call these important clones the SPCP clones. We rank SPCP clones considering their strength of evolutionary coupling. In our second study we further analyze evolutionary coupling of code clones with an aim to assist clone tracking. The purpose of clone tracking is to identify the co-change (i.e. changing together) candidates of code clones to ensure consistency of changes in the code-base. Our research in the second study identifies and ranks the important co-change candidates by analyzing their evolutionary coupling. In our third study we perform a deeper analysis on the SPCP clones and identify their cross-boundary evolutionary couplings. On the basis of such couplings we separate the SPCP clones into two disjoint subsets. While one subset contains the non-cross-boundary SPCP clones which can be considered important for refactoring, the other subset contains the cross-boundary SPCP clones which should be considered important for tracking. In our fourth study we analyze the bug-proneness of different types of SPCP clones in order to identify which type(s) of code clones have high tendencies of experiencing bug-fixes. Such clone-types can be given high priorities for management (refactoring or tracking). In our last study we analyze and compare the late propagation tendencies of different types of code clones. Late propagation is commonly regarded as a harmful clone evolution pattern. Findings from our last study can help us prioritize clone-types for management on the basis of their tendencies of experiencing late propagations. We also find that late propagation can be considerably minimized by managing the SPCP clones. On the basis of our studies we develop an automatic system called AMIC (Automatic Mining of Important Clones) that identifies the important clones for management (refactoring and tracking) and ranks these clones considering their evolutionary coupling, bug-proneness, and late propagation tendencies. We believe that our research findings have the potential to assist clone management by pin-pointing the important clones to be managed, and thus, considerably minimizing clone management effort

Modelling iteration in engineering design

This paper examines design iteration and its modelling in the simulation of New Product Development (NPD) processes. A framework comprising six perspectives of iteration is proposed and it is argued that the importance of each perspective depends upon domain-specific factors. Key challenges of modelling iteration in process simulation frameworks such as the Design Structure Matrix are discussed, and we argue that no single model or framework can fully capture the iterative dynamics of an NPD process. To conclude, we propose that consideration of iteration and its representation could help identify the most appropriate modelling framework for a given process and modelling objective, thereby improving the fidelity of design process simulation models and increasing their utility

Optimizing spread dynamics on graphs by message passing

Cascade processes are responsible for many important phenomena in natural and social sciences. Simple models of irreversible dynamics on graphs, in which nodes activate depending on the state of their neighbors, have been successfully applied to describe cascades in a large variety of contexts. Over the last decades, many efforts have been devoted to understand the typical behaviour of the cascades arising from initial conditions extracted at random from some given ensemble. However, the problem of optimizing the trajectory of the system, i.e. of identifying appropriate initial conditions to maximize (or minimize) the final number of active nodes, is still considered to be practically intractable, with the only exception of models that satisfy a sort of diminishing returns property called submodularity. Submodular models can be approximately solved by means of greedy strategies, but by definition they lack cooperative characteristics which are fundamental in many real systems. Here we introduce an efficient algorithm based on statistical physics for the optimization of trajectories in cascade processes on graphs. We show that for a wide class of irreversible dynamics, even in the absence of submodularity, the spread optimization problem can be solved efficiently on large networks. Analytic and algorithmic results on random graphs are complemented by the solution of the spread maximization problem on a real-world network (the Epinions consumer reviews network).Comment: Replacement for "The Spread Optimization Problem

Comparative Analysis of Requirements Change Prediction Models: Manual, Linguistic, and Neural Network

Requirement change propagation, if not managed, may lead to monetary losses or project failure. The a posteriori tracking of requirement dependencies is a well-established practice in project and change management. The identification of these dependencies often requires manual input by one or more individuals with intimate knowledge of the project. Moreover, the definition of these dependencies that help to predict requirement change is not currently found in the literature. This paper presents two industry case studies of predicting system requirement change propagation through three approaches: manually, linguistically, and bag-of-words. Dependencies are manually and automatically developed between requirements from textual data and computationally processed to develop surrogate models to predict change. Two types of relationship generation, manual keyword selection and part-of-speech tagging, are compared. Artificial neural networks are used to create surrogate models to predict change. These approaches are evaluated on three connectedness metrics: shortest path, path count, and maximum flow rate. The results are given in terms of search depth needed within a requirements document to identify the subsequent changes. The semi-automated approach yielded the most accurate results, requiring a search depth of 11 %, but sacrifices on automation. The fully automated approach is able to predict requirement change within a search depth of 15 % and offers the benefits of full minimal human input

Connectivity as the capacity to improve an organization’s decision-making

This paper describes the development of a new computational model to predict the desirability of decision consequences in an organization, and the development of a prototype tool to enable real-time interaction and decision support when changes occur simultaneously. A tool, called Decision Propagation System, is developed in response to the needs of BT Group plc in understanding the most effective set of interventions in the organization where the high degree of connectivity between system components and the uncertainty in connectivity data are two critical issues. Designed on a case study of the Fields Operations Engineering, this research demonstrates that a knowledge of overlapping decision propagation paths can direct the organizational decisions towards mitigating the risk of unintended consequences

Connectivity as the capacity to improve an organization’s decision-making

This paper describes the development of a new computational model to predict the desirability of decision consequences in an organization, and the development of a prototype tool to enable real-time interaction and decision support when changes occur simultaneously. A tool, called Decision Propagation System, is developed in response to the needs of BT Group plc in understanding the most effective set of interventions in the organization where the high degree of connectivity between system components and the uncertainty in connectivity data are two critical issues. Designed on a case study of the Fields Operations Engineering, this research demonstrates that a knowledge of overlapping decision propagation paths can direct the organizational decisions towards mitigating the risk of unintended consequences

Contribution to engineering change management in product development projects (reference models and multi-agent-based simulation)

L objectif de cette thèse est de fournir des modèles de référence, les méthodes de soutien et des outils qui simulent les change propagations dans un projet de développement de produit (PD) pour aider les prises de décision. Nous établissons d abord un cadre d analyse des changes de modéliser le contexte du change apparition et la propagation en prenant en compte les multiples domaines du projet PD simultané- ment. Dans le cadre, nous proposons les modèles conceptuels de change apparition et la propagation qui fournissent une méthode pour identifier les change et propagation et impliquent certaines caractéristiques du change propagations. S appuyant sur cela, nous vous proposons les procédures de mise en place des réseaux de propagation. Au sein du réseau, nous proposons la méthodologie de simulation de l évolution propaga- tions, puis de présenter le processus de mise en uvre des méthodologies et des modèles comme un prototype en utilisant la technologie à base multi-agents.The overall goal of this Ph.D. research is to provide reference models, support me- thods and tools that simulate change propagations in a Product Development (PD) project to assist decision-makings. We firstly establish a change analysis framework of modeling the context of change occurrence and propagation by taking into account the multiple knowledge areas of PD project simultaneously. Under the framework, we propose the conceptual models of change occurrence and change propagation that pro- vide a qualitative method to identify change and change propagation and imply some characteristics of change propagations. Relying on that, we suggest the procedures of building up the change propagation networks. Within the network, we propose the methodology of simulating change propagations and then present the process of im- plementing the methodologies and the models as a software prototype by using multi- agent based technology.BORDEAUX1-Bib.electronique (335229901) / SudocSudocFranceF

Developing Automotive Products Using the EAST-ADL2, an AUTOSAR Compliant Architecture Description Language

International audienceCurrent development trends in automotive software feature increasing standardization of the embedded software structure. But it still remains the critical issue of the overall engineering information management to control the system definition and manage its complexity. System modeling based onan Architecture Description Language (ADL) is a way to keep these assets within one information structure. The original EAST-ADL was developed in the EAST-EEA project (www.east-eea.org) and basic concepts were reused in the AUTOSAR standardization initiative. The original EAST-ADL is currently refined in the ATESST project (www.atesst.org) to EAST-ADL2. This paper presents the results of the language extension provided by the EAST-ADL2 domain model and focuses on its possible extension of the AUTOSAR standard to support decomposition of E/E automotive systems

Automatic design structure matrices: A comparison of two formula student projects

Design Structure Matrices have become a fundamental tool to support engineers in their handling and management of interactions across product & organisational architectures. Recent work in the field has exploited the opportunity afforded by Product Lifecycle Management systems, which capture the digital footprint of engineering projects to generate Design Structure Matrices in real-time through the co-occurrence of edits to product models. Given the systematic and more objective nature of the generation of these DSMs as well as being able to monitor their evolution throughout engineering projects, there now lies an opportunity in comparing projects/products using DSMs. To investigate this and the potential insights that could be generated, this paper presents the automatic generation of DSMs for two Formula Student projects. These have then been compared with respect to the end-of-project, change propagation characteristics and evolution of the DSMs. From this analysis, six insights have been generated that map the characteristics of the DSMs to the performance of the project/product and highlights the potential of automatic DSMs to further support engineering project management