Dimitrios Settas

Using Bayesian Belief Networks to Model Software Project Management Antipatterns

In spite of numerous traditional and agile software project management models proposed, process and project modeling still remains an open issue. This paper proposes a Bayesian network (BN) approach for modeling software project management antipatterns. This approach provides a framework for project managers, who would like to model the cause-effect relationships that underlie an antipattern, taking into account the inherent uncertainty of a software project. The approach is exemplified through a specific BN model of an antipattern. The antipattern is modeled using the empirical results of a controlled experiment on extreme programming (XP) that investigated the impact of developer personalities and temperaments on communication, collaboration-pair viability and effectiveness in pair programming. The resulting BN model provides the precise mathematical model of a project management antipattern and can be used to measure and handle uncertainty in mathematical terms

Enhancing ontology-based antipattern detection using Bayesian networks

Highlights? The antipatterns OWL ontology contains sources of uncertainty. ? Ontologies cannot represent uncertainty. ? Probabilistic antipattern information can be added in the ontology. ? BNTab Protege plug-in allows ontology based Bayesian network generation. 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.

SPARSE: A symptom-based antipattern retrieval knowledge-based system using Semantic Web technologies

Abstract Antipatterns provide information on commonly occurring solutions to problems that generate negative consequences. The number of software project management antipatterns that appears in the literature and the Web increases to the extent that makes using antipatterns problematic. Furthermore, antipatterns are usually inter-related and rarely appear in isolation. As a result, detecting which antipatterns exist in a software project is a challenging task which requires expert knowledge. This paper proposes SPARSE, an OWL ontology based knowledge-based system that aims to assist software project managers in the antipattern detection process. The antipattern ontology documents antipatterns and how they are related with other antipatterns through their causes, symptoms and consequences. The semantic relationships that derive from the antipattern definitions are determined using the Pellet DL reasoner and they are transformed into the COOL language of the CLIPS production rule engine. The purpose of this transformation is to create a compact representation of the antipattern knowledge, enabling a set of object-oriented CLIPS production rules to run and retrieve antipatterns relevant to some initial symptoms. SPARSE is exemplified through 31 OWL ontology antipattern instances of software development antipatterns that appear on the Web.

Towards a Dynamic Ontology Based Software Project Management Antipattern Intelligent System

The Software Project Management Antipattern Intelligent System (PROMAISE) is proposed as a Web-enabled knowledge-base framework that uses antipattern OWL ontologies in order to provide intelligent and up to date advice to software project managers regarding the selection of appropriate antipatterns in a software project. Antipatterns provide information on commonly occurring solutions to problems that generate negative consequences. These mechanisms are documented using informal paper based structures that do not readily support knowledge sharing and reuse. Antipattern OWL ontologies can be used to build a dynamic antipattern knowledge base, which can update itself automatically. This will allow the accessibility and transferability of up-to-date computer-mediated software project management knowledge to software project managers by encoding antipatterns into computer understandable ontologies. PROMAISE can function with this knowledge base in order to assist software project managers in the process of selecting applicable antipatterns.

Detecting similarities in antipattern ontologies using semantic social networks: Implications for software project management

Ontology has been recently proposed as an appropriate formalism to model software project management antipatterns, in order to encode antipatterns in a computer understandable form and introduce antipatterns to the Semantic Web. However, given two antipattern ontologies, the same entity can be described using different terminology. Therefore, the detection of similar antipattern ontologies is a difficult task. In this paper, we introduce a three-layered antipattern semantic social network, which involves the social network, the antipattern ontology network and the concept network. Social Network Analysis (SNA) techniques can be used to assist software project managers in finding similar antipattern ontologies. For this purpose, SNA measures are extracted from one layer of the semantic social network to another and this knowledge is used to infer new links between antipattern ontologies. The level of uncertainty associated with each new link is represented using Bayesian Networks (BNs). Furthermore, BNs address the issue of quantifying the uncertainty of the data collected regarding antipattern ontologies for the purposes of the conducted analysis. Finally, BNs are used to augment SNA by taking into account meta-information in their calculations. Hence, other knowledge not included in the social network can be used in order to search the social network for further inference. The benefits of using an antipattern semantic social network are illustrated using an example community of software project management antipattern ontologies.

Teaching Software Project Management through Management Antipatterns

Technological development and social progress are bound to affect fundamentally the business environments and therefore the people who work in them. The new conditions create unexpected difficulties and expectations that often limit the performance of the employees and consequently of the software projects, which often fail to meet their goals. The integrated education of future administrators - managers becomes a dynamic challenge to the new environments, which can be achieved by proposing new concepts such as Management Antipatterns. In this work, important conclusions and suggestions are made about the importance of effective and innovative education of future managers and their IT projects.

Resolving Complexity and Interdependence in Software Project Management Antipatterns Using the Dependency Structure Matrix

Software project management antipatterns are usually related to other antipatterns and rarely appear in isolation. This fact introduces inevitable interdependence and complexity that can not be addressed using existing formalisms. To reduce this complexity and interdependence, this paper proposes the Dependency Structure Matrix (DSM) as a method that visualizes and analyzes the dependencies between related attributes of software project management antipatterns. Furthermore, DSM provides a methodology that can be used to visualize three different configurations that characterize antipatterns and resolve cyclic dependencies that are formed between interdependent antipattern attributes. The proposed framework can be used by software project managers in order to resolve antipatterns that occur in a software project in a timely manner. The approach is exemplified through a DSM of 25 attributes of 16 related software project management antipatterns that appear in the literature and the Web.

Detecting Antipatterns Using a Web-Based Collaborative Antipattern Ontology Knowledge Base

The enrichment of the antipattern ontology that acts as the lexicon of terms to communicate antipatterns between people and software tools, is a labor intensive task. Existing work has implemented SPARSE, an ontology based intelligent system that uses a symptom based approach in order to semantically detect and retrieve inter-related antipatterns that exist in a software project. In this paper, we propose a Web-based environment that uses the Protege platform, in order to allow collaborative ontology editing as well as annotation and voting of both ontology components and ontology changes. This technology allows multiple users to edit and enrich the antipattern ontology simultaneously. Preliminary results on SPARSE show the effectiveness of the antipattern detection process during the research and development of a software project.

An ontology based e-learning system using antipatterns

Antipatterns are mechanisms that describe how to arrive at a good (refactored) solution from a fallacious solution that has negative consequences. These mechanisms are used in a variety of computer science topics and although their integration in teaching and computer science curriculum has been proposed, the development of an e-learning system using antipatterns, still remains an open issue. Previous work has proposed the use of WebProtege, a Web-based environment that allows collaborative editing as well as annotation and voting of both components and changes of the antipattern ontology. This ontology has been implemented as the knowledge base of SPARSE, an intelligent system that uses semantic web tools and techniques in order to detect the antipatterns that exist in a software project. In this paper, we leverage this semantic web technology and the formalism of ontology in order to propose a peer-production based e-learning system for the electronically supported learning of antipatterns. We illustrate how this Web-based system can transfer antipattern knowledge using an e-learning scenario as an example.

Towards Automatic Generation of Ontology-Based Antipattern Bayesian Network Models

Previous work has proposed the ontology-based semi-automatic generation of antipattern Bayesian Network(BN) models. The generated BN model can be used to illustrate the effects of uncertainty on antipatterns using Bayesian propagation. This can guide users in detecting particular antipattern attributes of importance based on uncertain ontological information. However, the proposed approach has been implemented in the Protege ontology editor environment and requires human intervention to specify how the BN model will be generated. The fully automated generation of ontology-based antipattern BN models still remains an open issue. SPARSE is an OWL ontology based intelligent system that assists software project managers in the antipattern detection process. In this paper, we propose the use of the resulting detected antipatterns of SPARSE, their attributes (i.e. causes, symptoms, consequences) and the ontological relationships between these attributes, in order to automatically generate BN models of the detected antipatterns. We illustrate how this approach can be implemented using an example of 8 antipattern attributes of 6 inter-related antipatterns detected using SPARSE.