Predicting Change Impact in Architecture Design with Bayesian Belief Networks

Large systems often have a long life-span and their system and software architecture design comprise many intricately related elements. The verification and maintenance of these architecture designs require an understanding of how and why the system are constructed. Design rationale is the reasoning behind a design and it provides an explanation of the design. However, the reasoning is often undocumented or unstructured in practice. This causes difficulties in the understanding of the original design, and makes it hard to detect inconsistencies, omissions and conflicts without any explanations to the intricacies of the design. Research into design rationale in the past has focused on argumentation-based design deliberations. Argumentation-based design rationale models provide an explicit representation of design rationale. However, these methods are ineffective in communicating design reasoning in practice because they do not support tracing to design elements and requirements in an effective manner. In this thesis, we firstly report a survey of practising architects to understand their perception of the value of design rationale and how they use and document this knowledge. From the survey, we have discovered that practitioners recognize the importance of documenting design rationale and frequently use them to reason about their design choices. However, they have indicated certain barriers to the use and documentation of design rationale. The results have indicated that there is no systematic approach to using and capturing design rationale in current architecture design practice. Using these findings, we address the issues of representing and applying architecture design rationale. We have constructed a rationale-based architecture model to represent design rationale, design objects and their relationships, which we call Architecture Rationale and Element Linkage (AREL). AREL captures both qualitative and quantitative rationale for architecture design. Quantitative rationale uses costs, benefits and risks to justify architecture decisions. Qualitative rationale documents the issues, arguments, alternatives and tradeoffs of a design decision. With the quantitative and qualitative rationale, the AREL model provides reasoning support to explain why architecture elements exist and what assumptions and constraints they depend on. Using a causal relationship in the AREL model, architecture decisions and architecture elements are linked together to explain the reasoning of the architecture design. Architecture Rationalisation Method (ARM) is a methodology that makes use of AREL to facilitate architecture design. ARM uses cost, benefit and risk as fundamental elements to rank and compare alternative solutions in the decision making process. Using the AREL model, we have proposed traceability and probabilistic techniques based on Bayesian Belief Networks (BBN) to support architecture understanding and maintenance. These techniques can help to carry out change impact analysis and rootcause analysis. The traceability techniques comprise of forward, backward and evolution tracings. Architects can trace the architecture design to discover the change impacts by analysing the qualitative reasons and the relationships in the architecture design. We have integrated BBN to AREL to provide an additional method where probability is used to evaluate and reason about the change impacts in the architecture design. This integration provides quantifiable support to AREL to perform predictive, diagnostic and combined reasoning. In order to align closely with industry practices, we have chosen to represent the rationale-based architecture model in UML. In a case study, the AREL model is applied retrospectively to a real-life bank payment systems to demonstrate its features and applications. Practising architects who are experts in the electronic payment system domain have been invited to evaluate the case study. They have found that AREL is useful in helping them understand the system architecture when they compared AREL with traditional design specifications. They have commented that AREL can be useful to support the verification and maintenance of the architecture because architects do not need to reconstruct or second-guess the design reasoning. We have implemented an AREL tool-set that is comprised of commercially available and custom-developed programs. It enables the capture of architecture design and its design rationale using a commercially available UML tool. It checks the well-formedness of an AREL model. It integrates a commercially available BBN tool to reason about the architecture design and to estimate its change impacts.

Bayesian belief networks (BBN) are a widely studied graphical model for representing uncertainty and probabilistic interdependence among variables. One of the factors that restricts the model's wide acceptance in practical applications is that the general inference with BBN is NP-hard. This is also true for the maximum a posteriori probability (MAP) problem, which is to find the most probable joint value assignment to all uninstantiated variables, given instantiation of some variables in a BBN. To circumvent the difficulty caused by MAP's computational complexity, we suggest in this paper a neural network approximation approach. With this approach, a BBN is treated as a neural network without any change or transformation of the network structure, and the node activation functions are derived based on an energy function defined over a given BBN. Three methods are developed. They are the hill-climbing style discrete method, the simulated annealing method, and the continuous method based on the mean field theory. All three methods are for BBN of general structures, with the restriction that nodes of BBN are binary variables. In addition, rules for applying these methods to noisy-or networks are also developed, which may lead to more efficient computation in some cases. These methods' convergence is analyzed, and their validity tested through a series of computer experiments with two BBN of moderate size and complexity. Although additional theoretical and empirical work is needed, the analysis and experiments suggest that this approach may lead to effective and accurate approximation for MAP problems.

The research on The Initial Survey of Evolution of Adaptive Reuse of Historic Buildings in Thailand is aimed at constructing knowledge for the module named Reuse and Rehabilitation of Historic Buildings. This module is part of the curriculum of Bachelor of Architecture, which studies the role of architecture fabricated within historical buildings. In the era between 1997 and 2012 in Thailand, there was a notable transformation from conservation to contemporary conversion. A review of conservation perspectives indicates a combined multi-disciplinary cooperation between architectural design and conservation. To establish issues regarding the knowledge of conversion, a research question is raised: how do architectural elements play its role through changing of use? Aimed at understanding the complexity among conversion ideologies, issues surrounding architectural elements of historical buildings are rationally explored. Based on significant conversion projects from 1996 to 2012, architectural elements were referenced as to how concepts and objectives were associated. Qualitative research was conducted through a study of primary sources; survey and classification of representative samples, and secondary documents, records and architectural drawings. The controlling significance of the buildings led to a discussion and an analysis of the architectural designs through new additions and amendments made on the historical fabric. Included in this discussion are the principles of conversion as they relate to the architecture of historic buildings and the ideology of the modification. It is found that understanding a change to architectural elements through an ontological perspective, that of attachment and associational approaches, could clearly reveal the construction programme that felicitates the historical building for which conservation or adaptation is determined. A dialogue on relevant contexts surrounding amendments of architectural elements demonstrated that a strong emphasis towards particular objectives of use could coincidentally harm the historical building’s architectural dimension framework. This leads to the notion that architecture for adaptive reuse should include knowledge of the original construction, a balance among conditions of the existing building, its programming and further habitation.

Visualization of architectural design decision (ADD) and its rationale, as a kind of traceability information, is supposed to facilitate the understanding of architecture design and the reasoning behind the design rationale, which is supposed to improve the architecting process and gets better architecture design results. But the lack of empirical evaluation that supports this statement prevents industrial practitioners from using ADD visualization in their daily architecting activities. In this paper, we conducted a controlled experiment to investigate how visualization of ADD can improve the understanding of architecture design by using Compendium, an open source tool for rationale visualization. The preliminary results show that visualization of ADD and its rationale does not increase the total time for reading architecture document (including visualization diagrams of ADDs) plus designing task, and it improves the understanding of architects on existing architecture design with better new design results.

Amodel based on Bayesian belief network is put forward to solve the innovation performance problems of the urban subway construction safety in China. After a systematic analyzing of the influence factors on the technological innovation performance, 13 variables were chosen in the light of implementation factors, interference factors, mediation factors and control factors, by taking the application of building information model as a case study. The causal relationship between these variables was constructed by the cause and effect diagram of experts' prior knowledge, combined with the correlation analysis method to determine the Bayesian belief network system structure. Sample data was got through a structural questionnaire survey. It was fit by NETICA software to get the probability density of each node. The application results show that this method can achieve a certain quantitative analysis of the innovation performance of the subway construction safety technology, and has better compliance compared with actual engineering situation.

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool. Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model. Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN. Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

<p>Landslide hazard assessment in India using historical data faces three challenges: (i) difficulty of obtaining systematic landslide occurrence data; (ii) under-representation of small-scale landslides; (iii) lack of recording of the physical/anthropogenic influences on landsliding. Here we show development of a Bayesian Belief Network (BBN) for a multi-hazard landslide assessment using experts’ judgements. Experts were chosen based on their experience on landslides and/or in Darjeeling Himalayas. A BBN produces a probability estimation of possible events and is a graph containing a set of variables (nodes) and conditional (in)dependencies between the nodes (arcs).</p><p>To better understand the relative weighting of potential causes of landslides in our case study area -Darjeeling Himalayas- we carried out four steps. (<strong>Step 1</strong>) We reviewed 29 peer- and grey-literature sources to list 13 physical/anthropogenic variables that might influence landsliding. (<strong>Step 2</strong>) We interviewed 11 experts about the importance of these 13 variables and asked for additional potential variables (resulting in 35 variables). (<strong>Step 3</strong>) We used interviews plus questionnaire to ask 16 experts to rate each of the 35 variables (scale 1-10) as to their potential to influence landsliding. The experts also added 7 more variables (resulting in 46 variables). (<strong>Step 4</strong>) Based on the ratings and interviews, we chose 35 out of 46 variables as our BBN nodes and from these the BBN arcs. Examples of these variables include rainfall, wildfires, geological weathering, planned infrastructure loading, cultivation (planned/unplanned), railway/road construction changing slope angle (planned), relief, slope, soil cohesion. Based on this study, we found that judgement of local people/academicians/technical experts can be of help whilst developing a BBN structure, allowing us to calculate probabilistic relationships between the nodes in a BBN. This process, therefore, can be utilised for landslide-based multi-hazard assessment in low data regions.</p>

This paper presents methods for analyzing the topology of a Bayesian belief network created to qualify and quantify the strengths of investigative hypotheses and their supporting digital evidence. The methods, which enable investigators to systematically establish, demonstrate and challenge a Bayesian belief network, help provide a powerful framework for reasoning about digital evidence. The methods are applied to review a Bayesian belief network constructed for a criminal case involving BitTorrent file sharing, and explain the causal effects underlying the legal arguments.

A probabilistic approach to truth maintenance is presented, specifically geared for automated problem solvers based on Bayesian belief network (BN) technology. Nodes and links in a BN capture semantic relationships among various domain related concepts. In the absence of firmer knowledge, default assumptions provide the beliefs for some nodes in the BN. Before posting incoming evidence into a BN node, a truth maintenance procedure is invoked to check for information consistency between the node's current expected state and the new observed state. In case of inconsistency, the truth maintenance procedure revises some default assumptions, by isolating those nodes causing inconsistency, via a sensitivity analysis procedure that exploits the strengths of BN causal dependency. The approach is specifically targeted for trustworthy situation assessment in the context of a military stability and support operation (SASO) scenario.

The main purpose of this article is to discuss applicability of Bayesian belief networks (BBN) within the procedures of working-capital credit scoring conducted in commercial banks. A brief description of Bayesian formulation of causal dependence and its strength is given. Inferential and diagnostic features of BBN are illustrated using sample structure. As an example we present and compare results of estimating a credit risk using two techniques: traditional credit-scoring system and BBN structure.

Fog events occur at Melbourne Airport, Melbourne, Victoria, Australia, approximately 12 times each year. Unforecast events are costly to the aviation industry, cause disruption, and are a safety risk. Thus, there is a need to improve operational fog forecasting. However, fog events are difficult to forecast because of the complexity of the physical processes and the impact of local geography and weather elements. Bayesian networks (BNs) are a probabilistic reasoning tool widely used for prediction, diagnosis, and risk assessment in a range of application domains. Several BNs for probabilistic weather prediction have been previously reported, but to date none have included an explicit forecast decision component and none have been used for operational weather forecasting. A Bayesian decision network [Bayesian Objective Fog Forecast Information Network (BOFFIN)] has been developed for fog forecasting at Melbourne Airport based on 34 years’ worth of data (1972–2005). Parameters were calibrated to ensure that the network had equivalent or better performance to prior operational forecast methods, which led to its adoption as an operational decision support tool. The current study was undertaken to evaluate the operational use of the network by forecasters over an 8-yr period (2006–13). This evaluation shows significantly improved forecasting accuracy by the forecasters using the network, as compared with previous years. BOFFIN-Melbourne has been accepted by forecasters because of its skill, visualization, and explanation facilities, and because it offers forecasters control over inputs where a predictor is considered unreliable.

This study has to be considered as another step towards the proposal of assessment/predictive models in software quality. We consider in this work, that a probabilistic model using Bayesian nets constitutes an interesting alternative to non-probabilistic models suggested in the literature. Thus, we propose in this paper a probabilistic approach using Bayesian networks to analyze and predict change impact in object-oriented systems. An impact model is built and probabilities are assigned to network nodes. Data obtained from a real system are exploited to empirically study causality hypotheses between some software internal attributes and change impact. Several scenarios are executed on the network, and the obtained results confirm that coupling is a good indicator of change impact.

Bayesian belief network (BBN) is a framework for representation/inference of some knowledge with uncertainty. Since the process of constructing a BBN manually by experts is time-consuming in general, some method supporting the task is needed. We proposed an algorithm for acquiring some BBN automatically from finite examples based on minimum description length (MDL) principle. This paper addresses an improvement which relaxes a constraint that the original scheme held on the representation. In BBNs, attributes and stochastic dependencies between them are expressed as nodes and directed links connecting them, respectively, where each attribute may be a predicate, a numerical data, etc., and each dependence is numerically expressed as the conditional probability of one attribute given other attributes if their dependence exists. Therefore, in general, BBNs are represented in terms of the network structure and the conditional probabilities.

Short innovation cycles in software and hardware make architecture design a key issue in future development processes for embedded systems. The basis for architectural design decisions is a transparent architecture evaluation.Our model-based approach supports a uniform representation of hierarchies of quality attributes and an integration of different architecture evaluation techniques and methods. We present a metamodel for architecture evaluation as a basis for the precise description of the quality attribute structure and the evaluation methodology. By modelling architecture evaluation, the relationships between architectural elements and quality attributes and interdependencies between quality attributes can be represented and investigated. Thereby, the architecture exploration process with its evaluations, decisions, and optimizations is made explicit, traceable, and analyzable.KeywordsQuality RateQuality AttributeSoftware ArchitectureHardware ArchitectureHardware CostThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Integration of weather conditions for predicting microbial water quality using Bayesian Belief Networks