BDTEX: A GQM-based Bayesian approach for the detection of antipatterns

The presence of antipatterns can have a negative impact on the quality of a program. Consequently, their efficient detection has drawn the attention of both researchers and practitioners. However, most aspects of antipatterns are loosely specified because quality assessment is ultimately a human-centric process that requires contextual data. Consequently, there is always a degree of uncertainty on whether a class in a program is an antipattern or not. None of the existing automatic detection approaches handle the inherent uncertainty of the detection process. First, we present BDTEX (Bayesian Detection Expert), a Goal Question Metric (GQM) based approach to build Bayesian Belief Networks (BBNs) from the definitions of antipatterns. We discuss the advantages of BBNs over rule-based models and illustrate BDTEX on the Blob antipattern. Second, we validate BDTEX with three antipatterns: Blob, Functional Decomposition, and Spaghetti code, and two open-source programs: GanttProject v1.10.2 and Xerces v2.7.0. We also compare the results of BDTEX with those of another approach, DECOR, in terms of precision, recall, and utility. Finally, we also show the applicability of our approach in an industrial context using Eclipse JDT and JHotDraw and introduce a novel classification of antipatterns depending on the effort needed to map their definitions to automatic detection approaches.     

基于本体的智能信息检索系统

近年来,基于本体的智能信息检索系统已成为智能信息检索系统领域最为活跃的研究方向之一.如何利用本体进一步提高其检索性能和智能性,成为基于本体的智能信息检索系统的主要研究目标.从面向过程的角度,对近几年基于本体的智能信息检索系统的研究进展进行了综述,对其框架、所需本体知识的获取和使用、关键技术、性能评测等进行了前沿概括、比较和分析.最后,对基于本体的智能信息检索系统有待深入研究的难点和热点进行了展望.     

Uncertainty management with imprecise knowledge with application to design

Uncertainty management is critical to the effective use of knowledge-based systems in a wide variety of domains. Design is typical of these domains in that the implementation of a design in an artifact, the future environment for the artifact, and the component characteristics of the artifact are all uncertain. Existing probabilistic schemes to address the inherent uncertainty in areas like design assume precise knowledge of the probabilities of relevant events. This paper defines a probabilistic method for uncertainty management with imprecise inputs. The approach combines Bayesian inference networks and information theoretic inference procedures. The resulting scheme manages both imprecision and uncertainty in the problem domain. An application of the approach to materiel design is described.     

Information integration via hierarchical and hybrid bayesian networks

A collaboration scheme for information integration among multiple agencies (and/or various divisions within a single agency) is designed using hierarchical and hybrid Bayesian networks (HHBNs). In this scheme, raw information is represented by transactions (e.g., communication, travel, and financing) and information entities to be integrated are modeled as random variables (e.g., an event occurs, an effect exists, or an action is undertaken). Each random variable has certain states with probabilities assigned to them. Hierarchical is in terms of the model structure and hybrid stems from our usage of both general Bayesian networks (BNs) and hidden Markov models (HMMs, a special form of dynamic BNs). The general BNs are adopted in the top (decision) layer to address global assessment for a specific question (e.g., "Is target A under terrorist threat?" in the context of counterterrorism). HMMs function in the bottom (observation) layer to report processed evidence to the upper layer BN based on the local information available to a particular agency or a division. A software tool, termed the adaptive safety analysis and monitoring (ASAM) system, is developed to implement HHBNs for information integration either in a centralized or in a distributed fashion. As an example, a terrorist attack scenario gleaned from open sources is modeled and analyzed to illustrate the functionality of the proposed framework.     

贝叶斯网等价类学习算法

贝叶斯网用一种紧凑的形式表示联合概率分布,具有完备的语义和坚实的理论基础,目前已成为人工智能领域处理不确定性问题的最佳方法之一。贝叶斯网学习是其关键问题,传统学习方法存在如下不足：（1）随节点数增多非法结构以指数级增加,影响学习效率;（2）在等价结构之间进行打分搜索,影响收敛速度;（3）假设每个结构具有相同的先验概率,造成等价类中包含结构越多则先验概率越高。本文提出一种学习马尔科夫等价类算法,该算法基于骨架空间进行状态转换,利用从骨架空间到等价类空间的映 映射关系实现学习贝叶斯网等价类。实验数据证明,该方法可有效缩小搜索空间规模,相对于在有向图空间搜索的算法加快了算法的收敛速度,提高了执行效率。     

软件项目投资风险评价过程建模研究

提出了一种基于贝叶斯网络的软件项目投资风险评价模型。在建模过程中以样本数据集为基础进行贝叶斯网络参数学习,从而在软件项目的投资阶段建立更加符合实际项目特征的贝叶斯网络。同时,从算法精度以及算法收敛性这两个方面验证该参数学习过程的有效性。经实践检验,在软件项目投资过程中该风险评价模型能够向决策者提供准确的投资风险信息。     

一种改进的软件项目开发风险管理模型

该文提出了一种改进的软件项目开发风险管理模型。该模型在贝叶斯网络的建模过程中以样本数据集为基础进行结构学习和参数学习,建立更符合实际软件项目特征的贝叶斯网络。同时,进一步完善了软件项目开发风险管理流程,并利用贝叶斯网络的信念更新过程实现动态软件项目风险管理。经实践检验,该改进模型能够更有效地对软件项目开发过程中的风险进行管理,提高软件开发的成功率。     

Explanation of Bayesian networks and influence diagrams in Elvira.

Bayesian networks (BNs) and influence diagrams (IDs) are probabilistic graphical models that are widely used for building diagnosis- and decision-support expert systems. Explanation of both the model and the reasoning is important for debugging these models, alleviating users' reluctance to accept their advice, and using them as tutoring systems. This paper describes some explanation options for BNs and IDs that have been implemented in Elvira and how they have been used for building medical models and teaching probabilistic reasoning to pre- and postgraduate students.     

Bayesian networks for enhancement of requirements engineering: a literature review

Requirements analysis is the software engineering stage that is closest to the users’ world. It also involves tasks that are knowledge intensive. Thus, the use of Bayesian networks (BNs) to model this knowledge would be a valuable aid. These probabilistic models could manage the imprecision and ambiguities usually present in requirements engineering (RE). In this work, we conduct a literature review focusing on where and how BNs are applied on subareas of RE in order to identify which gaps remain uncovered and which methods might engineers employ to incorporate this intelligent technique into their own requirements processes. The scarcity of identified studies (there are only 20) suggests that not all RE areas have been properly investigated in the literature. The evidence available for adopting BNs into RE is sufficiently mature yet the methods applied are not easily translatable to other topics. Nonetheless, there are enough studies supporting the applicability of synergistic cooperation between RE and BNs. This work provides a background for understanding the current state of research encompassing RE and BNs. Functional, non-functional and -ilities requirements artifacts are enhanced by the use of BNs. These models were obtained by interacting with experts or by learning from databases. The most common criticism from the point of view of BN experts is that the models lack validation, whereas requirements engineers point to the lack of a clear application method for BNs and the lack of tools for incorporating them as built-in help functions.     

Inference in multiply sectioned Bayesian networks: methods and performance comparison.

This paper extends lazy propagation for inference in single-agent Bayesian networks (BNs) to multiagent lazy inference in multiply sectioned BNs (MSBNs). Two methods are proposed using distinct runtime structures. It was proved that the new methods are exact and efficient when the domain structure is sparse. Both improve space and time complexity more than the existing method, which allows multiagent probabilistic reasoning to be performed in much larger domains given the computational resource. The relative performances of the three methods are compared analytically and experimentally.     

因果关系,贝叶斯网络与认知图

因果关系在预测和推理中具有重要的作用.贝叶斯网络已被用于构建诊断和决策系 统.近年来模糊认知图得到了重视.模糊认知图为结构性知识与因果推理提供了又一个理论 框架.本文简单介绍贝叶斯网络与认知图及其推理方法在智能系统中的应用.     

Medicine expert system dynamic Bayesian Network and ontology based

The paper proposes an application framework to be used for medicine assisted diagnosis based on ontology and Bayesian Network (DBNO). There are two goals: (1) to separate the domain knowledge from the probabilistic information and (2) to create an intuitive user interface. The framework architecture has three layers: knowledge, uncertainty model and user interface. The contributions of the domain experts are decoupled, the ontology builder will create the domain concepts and relationships focusing on the domain knowledge only. The uncertainty model is Bayesian Network and the probabilities of the variables states are stored in a profile repository. The diagnostician will use the user interface feeded with the domain ontology and one uncertainty profile. The application was tested on a sample medicine model for the diagnose of heart disease.     

基于本体的通用匹配机制OGMM研究

针对海量信息的检索与维护问题,本文提出了一套基于本体的通用匹配机制OGMM。该机制通过统一的领域本体增强信息的语义特性与共享性,通过以知识推理和相容性为特征的本体匹配算法实现信息的智能匹配,最后通过提供适用不同应用域的本体操作接口实现服务的个性化。该机制具有通用、智能和便捷等特性,易于高效实现Web信息管理与智能检索服务。     

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.     

Probability and agents

To make sense of the information that agents gather from the Web, they need to reason about it. If the information is precise and correct, they can use engines such as theorem provers to reason logically and derive correct conclusions. Unfortunately, the information is often imprecise and uncertain, which means they will need a probabilistic approach. More than 150 years ago, George Boole presented the logic that bears his name. There is concern that classical logic is not sufficient to model how people do or should reason. Adopting a probabilistic approach in constructing software agents and multiagent systems simplifies some thorny problems and exposes some difficult issues that you might overlook if you used purely logical approaches or (worse!) let procedural matters monopolize design concerns. Assessing the quality of the information received from another agent is a major problem in an agent system. The authors describe Bayesian networks and illustrate how you can use them for information quality assessment     

Parallel cycle-based branch-and-bound method for Bayesian network learning

Pattern Analysis and Applications - Bayesian networks (BNs) are one of the most commonly used models for representing uncertainty in medical diagnosis. Learning the exact structure of a BN is a...     

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.     

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.     

基于本体的知识库模型研究

本体作为一种能在语义和知识层次上描述信息系统的概念模型建模工具,近年来在计算机的许多领域得到了广泛的应用。其中,基于本体的知识构建是一个复杂的系统工程。就目前的研究状况来说,主要是解决如何构建知识模型的问题。本文运用本体知识模型相关的理论,在分析了本体论、知识库和知识库系统概念的基础上探讨了本体论在知识库系统中的应用和方法,提出了一种基于本体的知识模型,并阐述了基于本体的知识库构建方法。     

Ontology-based computational intelligent multi-agent and its application to CMMI assessment

This study presents an ontology-based computational intelligent multi-agent system for Capability Maturity Model Integration (CMMI) assessment. An ontology model is developed to represent the CMMI domain knowledge that will be adopted by the computational intelligent multi-agent. The CMMI ontology is predefined by domain experts, and created by the ontology generating system. The computational intelligent multi-agent comprises a natural language processing agent, an ontological reasoning agent and a summary agent. The multi-agent deals with the evaluation reports from the natural language processing agent, infers the term relation strength between the ontology and the evaluation report, and then summarizes the main sentences of the evaluation report. The summary reports are meanwhile transmitted back to the domain expert, which makes the domain expert further adjust the CMMI ontology. Experimental results indicate that the ontology-based computational intelligent multi-agent can effectively summarize the evaluation reports for the CMMI assessment.