Enhancing ontology-based antipattern detection using Bayesian networks

Antipatterns provide information on commonly occurring solutions to problems that generate negative consequences. The antipattern ontology has been recently proposed as a knowledge base for SPARSE, an intelligent system that can detect the antipatterns that exist in a software project. However, apart from the plethora of antipatterns that are inherently informal and imprecise, the information used in the antipattern ontology itself is many times imprecise or vaguely defined. For example, the certainty in which a cause, symptom or consequence of an antipattern exists in a software project. Taking into account probabilistic information would yield more realistic, intelligent and effective ontology-based applications to support the technology of antipatterns. However, ontologies are not capable of representing uncertainty and the effective detection of antipatterns taking into account the uncertainty that exists in software project antipatterns still remains an open issue. Bayesian Networks (BNs) have been previously used in order to measure, illustrate and handle antipattern uncertainty in mathematical terms. In this paper, we explore the ways in which the antipattern ontology can be enhanced using Bayesian networks in order to reinforce the existing ontology-based detection process. This approach allows software developers to quantify the existence of an antipattern using Bayesian networks, based on probabilistic knowledge contained in the antipattern ontology regarding relationships of antipatterns through their causes, symptoms and consequences. The framework is exemplified using a Bayesian network model of 13 antipattern attributes, which is constructed using BNTab, a plug-in developed for the Protege ontology editor that generates BNs based on ontological information.     

Using model transformation to refactor use case models based on antipatterns

Use Case modeling is a popular technique for documenting functional requirements of software systems. Refactoring is the process of enhancing the structure of a software artifact without changing its intended behavior. Refactoring, which was first introduced for source code, has been extended for use case models. Antipatterns are low quality solutions to commonly occurring design problems. The presence of antipatterns in a use case model is likely to propagate defects to other software artifacts. Therefore, detection and refactoring of antipatterns in use case models is crucial for ensuring the overall quality of a software system. Model transformation can greatly ease several software development activities including model refactoring. In this paper, a model transformation approach is proposed for improving the quality of use case models. Model transformations which can detect antipattern instances in a given use case model, and refactor them appropriately are defined and implemented. The practicability of the approach is demonstrated by applying it on a case study that pertains to biodiversity database system. The results show that model transformations can efficiently improve quality of use case models by saving time and effort.     

DECOR: A Method for the Specification and Detection of Code and Design Smells

Code and design smells are poor solutions to recurring implementation and design problems. They may hinder the evolution of a system by making it hard for software engineers to carry out changes. We propose three contributions to the research field related to code and design smells: 1) Decor, a method that embodies and defines all the steps necessary for the specification and detection of code and design smells, 2) Detex, a detection technique that instantiates this method, and 3) an empirical validation in terms of precision and recall of Detex. The originality of Detex stems from the ability for software engineers to specify smells at a high level of abstraction using a consistent vocabulary and domain-specific language for automatically generating detection algorithms. Using Detex, we specify four well-known design smells: the antipatterns Blob, Functional Decomposition, Spaghetti Code, and Swiss Army Knife, and their 15 underlying code smells, and we automatically generate their detection algorithms. We apply and validate the detection algorithms in terms of precision and recall on Xerces v2.7.0, and discuss the precision of these algorithms on 11 open-source systems.     

On the use of Bayesian belief networks for the prediction of software productivity

In spite of numerous methods proposed, software cost estimation remains an open issue and in most situations expert judgment is still being used. In this paper, we propose the use of Bayesian belief networks (BBNs), already applied in other software engineering areas, to support expert judgment in software cost estimation. We briefly present BBNs and their advantages for expert opinion support and we propose their use for productivity estimation. We illustrate our approach by giving two examples, one based on the COCOMO81 cost factors and a second one, dealing with productivity in ERP system localization.     

Valuing Design Repair

One of the favorite activities in any of the architecture or design courses is to discuss antipatterns - design ideas hatched with good intentions that prove problematic over time. The few books on antipatterns focus primarily on introducing problems and straightforward solutions, which makes them hard to distinguish from better-known books that present design or programming guidelines or refactoring advice. However, there's a slight but significant difference between antipatterns and style guidance. A style guide typically covers good practices - what to do and what to avoid. An antipattern is somewhat more ambitious. It seeks to explain how good intentions can go awry and suggest meaningful ways to repair broken systems. The point isn't so much to say "do this" or "avoid doing that" as to suggest ways to prevent a problem or to skillfully apply a set of corrective actions.     

An approach for modeling and detecting software performance antipatterns based on first-order logics

The problem of interpreting the results of performance analysis is quite critical in the software performance domain. Mean values, variances and probability distributions are hard to interpret for providing feedback to software architects. Instead, what architects expect are solutions to performance problems, possibly in the form of architectural alternatives (e.g. split a software component in two components and re-deploy one of them). In a software performance engineering process, the path from analysis results to software design or implementation alternatives is still based on the skills and experience of analysts. In this paper, we propose an approach for the generation of feedback based on performance antipatterns. In particular, we focus on the representation and detection of antipatterns. To this goal, we model performance antipatterns as logical predicates and we build an engine, based on such predicates, aimed at detecting performance antipatterns in an XML representation of the software system. Finally, we show the approach at work on a case study.     

Guilt-based handling of software performance antipatterns in palladio architectural models

Antipatterns are conceptually similar to patterns in that they document recurring solutions to common design problems. Software performance antipatterns document common performance problems in the design as well as their solutions. The definition of performance antipatterns concerns software properties that can include static, dynamic, and deployment aspects. To make use of such knowledge, we propose an approach that helps software architects to identify and solve performance antipatterns. Our approach provides software performance feedback to architects, since it suggests the design alternatives that allow overcoming the detected performance problems.The feedback process may be quite complex since architects may have to assess several design options before achieving the architectural model that best fits the end-user expectations. In order to optimise such process we introduce a ranking methodology that identifies, among a set of detected antipatterns, the “guilty” ones, i.e. the antipatterns that more likely contribute to the violation of specific performance requirements. The introduction of our ranking process leads the system to converge towards the desired performance improvement by discarding a consistent part of design alternatives. Four case studies in different application domains have been used to assess the validity of the approach.     

Applying a validation framework to a working airport terminal model

Validation is an important issue in the development and application of Bayesian Belief Network (BBN) models, especially when the outcome of the model cannot be directly observed. Despite this, few frameworks for validating BBNs have been proposed and fewer have been applied to substantive real-world problems. In this paper we adopt the approach by Pitchforth and Mengersen (2013), which includes nine validation tests that each focus on the structure, discretisation, parameterisation and behaviour of the BBNs included in the case study.We describe the process and result of implementing a validation framework on a model of a real airport terminal system with particular reference to its effectiveness in producing a valid model that can be used and understood by operational decision makers. In applying the proposed validation framework we demonstrate the overall validity of the Inbound Passenger Facilitation Model as well as the effectiveness of the validity framework itself.     

An exploratory study of the impact of antipatterns on class change- and fault-proneness

Antipatterns are poor design choices that are conjectured to make object-oriented systems harder to maintain. We investigate the impact of antipatterns on classes in object-oriented systems by studying the relation between the presence of antipatterns and the change- and fault-proneness of the classes. We detect 13 antipatterns in 54 releases of ArgoUML, Eclipse, Mylyn, and Rhino, and analyse (1) to what extent classes participating in antipatterns have higher odds to change or to be subject to fault-fixing than other classes, (2) to what extent these odds (if higher) are due to the sizes of the classes or to the presence of antipatterns, and (3) what kinds of changes affect classes participating in antipatterns. We show that, in almost all releases of the four systems, classes participating in antipatterns are more change-and fault-prone than others. We also show that size alone cannot explain the higher odds of classes with antipatterns to underwent a (fault-fixing) change than other classes. Finally, we show that structural changes affect more classes with antipatterns than others. We provide qualitative explanations of the increase of change- and fault-proneness in classes participating in antipatterns using release notes and bug reports. The obtained results justify a posteriori previous work on the specification and detection of antipatterns and could help to better focus quality assurance and testing activities.     

A review of Bayesian belief networks in ecosystem service modelling

A wide range of quantitative and qualitative modelling research on ecosystem services (ESS) has recently been conducted. The available models range between elementary, indicator-based models and complex process-based systems. A semi-quantitative modelling approach that has recently gained importance in ecological modelling is Bayesian belief networks (BBNs). Due to their high transparency, the possibility to combine empirical data with expert knowledge and their explicit treatment of uncertainties, BBNs can make a considerable contribution to the ESS modelling research. However, the number of applications of BBNs in ESS modelling is still limited. This review discusses a number of BBN-based ESS models developed in the last decade. A SWOT analysis highlights the advantages and disadvantages of BBNs in ESS modelling and pinpoints remaining challenges for future research. The existing BBN models are suited to describe, analyse, predict and value ESS. Nevertheless, some weaknesses have to be considered, including poor flexibility of frequently applied software packages, difficulties in eliciting expert knowledge and the inability to model feedback loops.     

Bayesian decision theory for support vector machines: Imbalance measurement and feature optimization

Classification approaches usually present the poor generalization performance with an apparent class imbalance problem. Surely, a measures of the quality of the possible models reflected the remaining uncertainty in the class imbalance on learning. The purpose of our learning method is to lead an attractive pragmatic expansion scheme of the Bayesian approach to assess how well it is aligned with the class imbalance problem. Thus, we propose a method with a model assessment of the interplay between various classification decisions using probability, corresponding decision costs, and quadratic program of optimal margin classifier called: Bayesian Support Vector Machines (BSVMs) learning strategy. In the framework, we did modify in the objects and conditions of primal problem to reproduce an appropriate learning rule for an observation sample. The experiments on several existing data sets showed that BSVMs may appropriately capture the true relationship between the inputs and outputs by experimental evidence.     

A Bayesian analysis of spherical pattern based on finite Langevin mixture

Parameter estimation is a cornerstone of most fundamental problems of statistical research and practice. In particular, finite mixture models have long been heavily relied on deterministic approaches such as expectation maximization (EM). Despite their successful utilization in wide spectrum of areas, they have inclined to converge to local solutions. An alternative approach is the adoption of Bayesian inference that naturally addresses data uncertainty while ensuring good generalization. To this end, in this paper we propose a fully Bayesian approach for Langevin mixture model estimation and selection via MCMC algorithm based on Gibbs sampler, Metropolis–Hastings and Bayes factors. We demonstrate the effectiveness and the merits of the proposed learning framework through synthetic data and challenging applications involving topic detection and tracking and image categorization.     

A Bayesian network approach to assess and predict software quality using activity-based quality models

ContextSoftware quality is a complex concept. Therefore, assessing and predicting it is still challenging in practice as well as in research. Activity-based quality models break down this complex concept into concrete definitions, more precisely facts about the system, process, and environment as well as their impact on activities performed on and with the system. However, these models lack an operationalisation that would allow them to be used in assessment and prediction of quality. Bayesian networks have been shown to be a viable means for this task incorporating variables with uncertainty.ObjectiveThe qualitative knowledge contained in activity-based quality models are an abundant basis for building Bayesian networks for quality assessment. This paper describes a four-step approach for deriving systematically a Bayesian network from an assessment goal and a quality model.MethodThe four steps of the approach are explained in detail and with running examples. Furthermore, an initial evaluation is performed, in which data from NASA projects and an open source system is obtained. The approach is applied to this data and its applicability is analysed.ResultsThe approach is applicable to the data from the NASA projects and the open source system. However, the predictive results vary depending on the availability and quality of the data, especially the underlying general distributions.ConclusionThe approach is viable in a realistic context but needs further investigation in case studies in order to analyse its predictive validity.     

Applications of Bayesian belief networks in water resource management: A systematic review

Bayesian belief networks (BBNs) are probabilistic graphical models that can capture and integrate both quantitative and qualitative data, thus accommodating data-limited conditions. This paper systematically reviews applications of BBNs with respect to spatial factors, water domains, and the consideration of climate change impacts. The methods used for constructing and validating BBN models, and their applications in different forms of decision-making support are examined. Most reviewed publications originate from developed countries (70%), in temperate climate zones (42%), and focus mainly on water quality (42%). In 60% of the reviewed applications model validation was based on the expert or stakeholder evaluation and sensitivity analysis, and whilst in 27% model performance was not discussed. Most reviewed articles applied BBNs in strategic decision-making contexts (52%). Integrated modelling tools for addressing challenges of dynamically complex systems were also reviewed by analysing the strengths and weaknesses of BBNs, and integration of BBNs with other modelling tools.     

Bayesian optimization for learning gaits under uncertainty

Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parametrization, finding near-optimal parameters can be daunting. Typically, this kind of parameter optimization requires specific expert knowledge and extensive robot experiments. Automatic black-box gait optimization methods greatly reduce the need for human expertise and time-consuming design processes. Many different approaches for automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this article, we thoroughly discuss multiple automatic optimization methods in the context of gait optimization. We extensively evaluate Bayesian optimization, a model-based approach to black-box optimization under uncertainty, on both simulated problems and real robots. This evaluation demonstrates that Bayesian optimization is particularly suited for robotic applications, where it is crucial to find a good set of gait parameters in a small number of experiments.     

Partial abductive inference in Bayesian belief networks - anevolutionary computation approach by using problem-specific geneticoperators

Abductive inference in Bayesian belief networks (BBNs) is intended as the process of generating the K most probable configurations given observed evidence. When we are interested only in a subset of the network's variables, this problem is called partial abductive inference. Both problems are NP-hard, and so exact computation is not always possible. In this paper, a genetic algorithm is used to perform partial abductive inference in BBNs. The main contribution is the introduction of new genetic operators designed specifically for this problem. By using these genetic operators, we try to take advantage of the calculations previously carried out, when a new individual is evaluated. The algorithm is tested using a widely-used Bayesian network and a randomly generated one, and then compared with a previous genetic algorithm based on classical genetic operators. From the experimental results, we conclude that the new genetic operators preserve the accuracy of the previous algorithm and also reduce the number of operations performed during the evaluation of individuals. The performance of the genetic algorithm is thus improved     

Urban runoff modelling uncertainty: Comparison among Bayesian and pseudo-Bayesian methods

Urban stormwater quality modelling plays a central role in evaluation of the quality of the receiving water body. However, the complexity of the physical processes that must be simulated and the limited amount of data available for calibration may lead to high uncertainty in the model results. This study was conducted to assess modelling uncertainty associated with catchment surface pollution evaluation. Eight models were compared based on the results of a case study in which there was limited data available for calibration. Uncertainty analysis was then conducted using three different methods: the Bayesian Monte Carlo method, the GLUE pseudo-Bayesian method and the GLUE method revised by means of a formal distribution of residuals between the model and measured data (GLUE_f). The uncertainty assessment of the models enabled evaluation of the advantages and limitations of the three methodologies adopted. The models were then tested using the quantity–quality data gathered for the Fossolo catchment in Bologna, Italy. The results revealed that all of the models evaluated here provided good calibration results, even if the model reliability (in terms of related uncertainty) varied, which suggests the adoption of a specific modelling approach with respect to the others. Additionally, a comparison of uncertainty analysis approaches showed that, regarding the models evaluated here, the classical Bayesian method is more effective at discriminating models according to their uncertainty, but the GLUE approach performs similarly when it is based on the same founding assumptions as the Bayesian method.     

机器学习与生物信息学

后基因时代，探索和解释隐藏在分子生物学数据库中的有用信息将是对生物信息学研究人员的巨大挑战!为了解决分子生物学中遇到的这些难题，有效及廉价的方法是非常必要的．机器学习是一种自动的、具有智能学习技术的方法，有助于达到上述目的。本文就知识发现、人工神经网络、决策树、贝叶斯网络、遗传算法、隐马尔科夫链、聚类、归纳逻辑编程、支持向量机等机器学习方法在生物信息学中的应用进行了系统地评述．这些方法有助于加速生物分子结构预测、基因发现、基因组学和蛋白组学等方面的研究进展．     

Software Design Smell Detection: a systematic mapping study

Design Smells are indicators of situations that negatively affect software quality attributes such as understandability, testability, extensibility, reusability, and maintainability in general. Improving maintainability is one of the cornerstones of making software evolution easier. Hence, Design Smell Detection is important in helping developers when making decisions that can improve software evolution processes. After a long period of research, it is important to organize the knowledge produced so far and to identify current challenges and future trends. In this paper, we analyze 18 years of research into Design Smell Detection. There is a wide variety of terms that have been used in the literature to describe concepts which are similar to what we have defined as “Design Smells,” such as design defect, design flaw, anomaly, pitfall, antipattern, and disharmony. The aim of this paper is to analyze all these terms and include them in the study. We have used the standard systematic literature review method based on a comprehensive set of 395 articles published in different proceedings, journals, and book chapters. We present the results in different dimensions of Design Smell Detection, such as the type or scope of smell, detection approaches, tools, applied techniques, validation evidence, type of artifact in which the smell is detected, resources used in evaluation, supported languages, and relation between detected smells and software quality attributes according to a quality model. The main contributions of this paper are, on the one hand, the application of domain modeling techniques to obtain a conceptual model that allows the organization of the knowledge on Design Smell Detection and a collaborative web application built on that knowledge and, on the other, finding how tendencies have moved across different kinds of smell detection, as well as different approaches and techniques. Key findings for future trends include the fact that all automatic detection tools described in the literature identify Design Smells as a binary decision (having the smell or not), which is an opportunity to evolve to fuzzy and prioritized decisions. We also find that there is a lack of human experts and benchmark validation processes, as well as demonstrating that Design Smell Detection positively influences quality attributes.

     

From a domain analysis to the specification and detection of code and design smells

Code and design smells are recurring design problems in software systems that must be identified to avoid their possible negative consequences on development and maintenance. Consequently, several smell detection approaches and tools have been proposed in the literature. However, so far, they allow the detection of predefined smells but the detection of new smells or smells adapted to the context of the analysed systems is possible only by implementing new detection algorithms manually. Moreover, previous approaches do not explain the transition from specifications of smells to their detection. Finally, the validation of the existing approaches and tools has been limited on few proprietary systems and on a reduced number of smells. In this paper, we introduce an approach to automate the generation of detection algorithms from specifications written using a domain-specific language. This language is defined from a thorough domain analysis. It allows the specification of smells using high-level domain-related abstractions. It allows the adaptation of the specifications of smells to the context of the analysed systems. We specify 10 smells, generate automatically their detection algorithms using templates, and validate the algorithms in terms of precision and recall on Xerces v2.7.0 and GanttProject v1.10.2, two open-source object-oriented systems. We also compare the detection results with those of a previous approach, iPlasma.