Rodrigo G. C. Rocha

An evidence‐based model of distributed software development project management: results from a systematic mapping study

Distributed software development (DSD) has intensified over the past few years, and DSD project management is more complex than collocated project management. However, no systematic research effort has focused on aggregating evidence from the scientific literature to build models to support project management on DSD context. For these reasons, the goal of this paper is to build an evidence‐based model of DSD project management from the research findings about challenges of DSD and the practices, models and tools proposed and used to overcome these challenges. We based the construction of this model on the evidence collected and synthesized by a comprehensive systematic mapping study of 70 research papers published between 1997 and 2009. We believe that our results can help practitioners and researchers to better understand the challenges and implement more effective solutions to improve project management within distributed project management teams. These results also provide a mapping of the research about DSD project management, identifying areas where further research is needed. Copyright

Ontologies supporting the distributed software development: a systematic mapping study

Background: Along the last decade, there has been a significant increase in the adoption of the approaches based on Distributed Software Development (DSD). This approach has brought several competitive advantages, as well as new challenges such as communication and information sharing. In this context, the ontologies can provide benefits such as the definition, standardization and sharing of knowledge involved in the project, allowing a uniform understanding of information and facilitating the collaboration among distributed software development teams. Aim: Identifying evidence to determine which tools, models, techniques and best practices that use ontologies to support the DSD projects, and which ontologies proposed in this context. Method: This paper presents a systematic mapping study conducted in order to investigate how ontologies are being applied as a support to the DSD. The research protocol was based on Kitchenhams, and Travassos and Biolchinis guidelines. Searches were performed both in manual and automatic way in a set of digital libraries engines and leading conferences in the Software Engineering field. Results: From the initial set of 1588 studies, it was selected a total of 38 primary studies that answer the two research questions. Conclusions: This work presents evidences from each paper collected and an analysis of results reached. The results support the foundation for proposing and developing a feature based on ontologies to support the DSD, besides encouraging further researches that may promote advancements in this area and fostering the adoption of these types of resources by the global software industry.

An Approach for Learning and Construction of Expressive Ontology from Text in Natural Language

In this paper, we present an approach based on Ontology Learning and Natural Language Processing for automatic construction of expressive Ontologies, specifically in OWL DL with ALC expressivity, from a natural language text. The viability of our approach is demonstrated through the generation of descriptions of complex axioms from concepts defined by users and glossaries found at Wikipedia. We evaluated our approach in an experiment with entry sentences enriched with hierarchy axioms, disjunction, conjunction, negation, as well as existential and universal quantification to impose restriction of properties. The obtained results prove that our model is an effective solution for knowledge representation and automatic construction of expressive Ontologies. Thereby, it assists professionals involved in processes for obtain, construct and model knowledge domain.

A Case-based Reasoning System to Support the Global Software Development

Abstract In addition to the benefits brought by the use of Distributed Software Development, new challenges linked to its use also emerged. Due to lack of information companies and organizations around the globe, independently, solve these challenges in many different ways, each with their practices, some more some less efficient, where best practices are hardly widespread among DDS community. In this context, this paper aims to present a web system based on Case Based Reasoning and Natural Language Processing to extract information in text form of problems and solutions adopted by distributed software projects and to recommend similar past experiences in order to support the decisions and resolutions of problems arising from new situations in distributed projects. The feasibility proof of the method was made from experimental tests conducted to identify the success of the recommendations of previous valid cases, with a success rate of 90% for the sample used.

A Proposal of an Ontology-Based System for Distributed Teams

Global Software Development enables new challenges to arise, such as the inexistence of a formal, normalized model of a projects data and artifacts accessible to all the individuals involved, which makes it harder for them to communicate, understand each other and what is specified on the projects artifacts. Then, this paper proposes a knowledge management tool that utilizes a domain-specific ontology for distributed development environments, aiming to help distributed teams overcome the challenges brought by this modality of software development proposing techniques and best practices. Thus, the main output of this work is Ontology-based System to Support the software development process with distributed teams.

Representing Knowledge in DL ALC from Text

Abstract In this paper, we present an approach based on Ontology Learning and Natural Language Processing for automatic construction of expressive Ontologies, specifically in OWL DL with ALC expressivity, from a natural language text. The viability of our approach is demonstrated through the generation of descriptions of complex axioms from concepts defined by users and glossaries found at Wikipedia. We evaluated our approach in an experiment with entry sentences enriched with hierarchy axioms, disjunction, conjunction, negation, as well as existential and universal quantification to impose restriction of properties. The obtained results prove that our model is an effective solution for knowledge representation and automatic construction of expressive Ontologies. Thereby, it assists professionals involved in processes for obtain, construct and model knowledge domain.

DKDOnto: An Ontology to Support Software Development with Distributed Teams

Abstract The Distributed Software Development has become an option for software companies to expand their perspective and work with dispersed teams, exploiting the advantages brought by this approach. However, this way of developing software enables new challenges to arise, such as the inexistence of a formal, normalized model of a project’s data and artifacts accessible to all the individuals involved, which makes it harder for them to communicate, understand each other and what is specified on the project’s artifacts. This paper proposes a knowledge base called DKDOnto, a domain-specific ontology for distributed development, aiming to help projects with a common vocabulary, allowing to assist better the distributed software development process.

A System Based on Ontology and Case-Based Reasoning to Support Distributed Teams

The intrinsic nature of distributed software development (DSD) brings new challenges, such as communication issues and sharing information efficiently. Software companies have a tendency to face these challenges using individual and isolated approaches, making difficult to spread good practices for the DSD community. In other contexts, concepts and techniques from Artificial Intelligence (AI) are frequently used in order to improve the functioning of systems and process. This work is based on the following AI concepts: ontologies, case-based reasoning (CBR) and natural language processing (NLP). We propose a system, based on ontology and case-based reasoning, that operates as follows: i) we use a tool for ontology storage, access and processing; and ii) an ontology-based CBR tool which aims to aid software companies by recommending techniques and best practices for minimizing or solving potential challenges that may be faced by DSD processes. The main results from this research are: i) a specific ontology for distributed software development teams; ii) a tool to facilitate the access and manipulation of the proposed ontology; and iii) a case based reasoning system that utilizes natural language processing. Initial results of the performed experiments indicate a success rate of 91.7% in the recommendation of solutions for potential problems coming from DSD processes.

Generating Description Logic \({\mathcal ALC}\) from Text in Natural Language

In this paper, we present a natural language translator for expressive ontologies and ensure that it is a viable solution to the automated acquisition of ontologies and complete axioms, constituting an effective solution for automating the expressive ontology building Process. The translator is based on syntactic and semantic text analysis. The viability of our approach is demonstrated through the generation of descriptions of complex axioms from concepts defined by users and glossaries found at Wikipedia. We evaluated our approach in an initial experiment with entry sentences enriched with hierarchy axioms, disjunction, conjunction, negation, as well as existential and universal quantification to impose restriction of properties.

An Approach for Automatic Expressive Ontology Construction from Natural Language

In this paper, we present an approach based on Ontology Learning and Natural Language Processing for automatic construction of expressive Ontologies, specifically in OWL DL with ALC expressivity, from a natural language text. The viability of our approach is demonstrated through the generation of descriptions of complex axioms from concepts defined by users and glossaries found at Wikipedia. We evaluated our approach in an experiment with entry sentences enriched with hierarchy axioms, disjunction, conjunction, negation, as well as existential and universal quantification to impose restriction of properties. The obtained results prove that our model is an effective solution for knowledge representation and automatic construction of expressive Ontologies. Thereby, it assists professionals involved in processes for obtain, construct and model knowledge domain.