José Luís Braga

Modeling and scenario simulation for decision support in management of requirements activities in software projects

There are many tools and techniques readily available to support the work in requirements activities of software development processes. As a consequence, the high frequency of errors still occurring in requirements activities suggests that the misunderstanding of the relationships among key decisions is the probable reason for this. The present work presents a system dynamics model constructed to make it possible for users to better understand the relations among key decision variables in requirements activities. The model was parameterized with data taken from previous studies and from a software development company so as to run two sets of simulations with three scenarios each. Optimistic, baseline and pessimistic scenarios are created on the basis of different assumptions regarding risk factors related to requirements volatility and people turnover. We used our simulation results to foresee the effects of these risk factors on the quality and cost of work in requirements activities. Up-to-date results from the software engineering literature strongly support the simulation outcomes obtained in our research. Copyright

Selection of Software Development Good Practices in Micro and Small Enterprises: An Approach Using Knowledge-Based Systems

Currently, companies should turn their attention to the increasing market competition. The quality of their products is directly related to the organizational processes that should be well defined and adopted. Regarding software, it is important that the choice of a development method is aimed at matching organization needs and its production culture. The micro and small enterprises face many problems and one of the largest ones is the lack of policies that can help improving development processes. This paper presents and describes a knowledge-based system that is able to suggest a set of good practices in software development that closely match company needs and culture represented as a profile. The work exposed here is an initial step towards an automation process for the selection of good practices based on a company profile.

Searching for metadata using knowledge bases and topic maps in Spatial Data Infrastructures

Concern for environmental issues has become a reality in all sectors of society, mainly among researchers and professionals who work directly with environmental status. In this context, several studies have been undertaken on sustainable development of the Brazilian Amazon, generating a large amount of data and information. Environmental area characterization involves the knowledge about their natural, economic and social resources, as well as understanding the interaction and correlation among them. Such interdisciplinary character requires new solutions for knowledge representation. This study proposes to minimize metadata recovery problems in Spatial Data Infrastructures by using Topic Maps and Thesaurus. This approach applied to an interface aims to allow users to visually recover information from metadata catalogs.

A Dublin core application profile for documenting analysis patterns in a reuse infrastructure

Analysis patterns are reusable computational artefacts aimed at the analysis stage of the software development process. Although analysis patterns can facilitate the work of analysts and developers, the access to them is still very poor because of the way they are usually described and made available. In order to reduce these limitations as well as to support the cataloguing and to encourage the reuse of analysis patterns, the Analysis Patterns Reuse Infrastructure (APRI) was proposed. This infrastructure comprises a repository of analysis patterns documented through a specific metadata profile that can be accessed via web services. Based on the proposal of APRI, this paper introduces a machine-processable metadata profile suited to the documentation of analysis patterns, called Dublin Core Application Profile for Analysis Patterns (DC2AP). This metadata profile is described by RDF files identified via URI, thus providing linked data that increase the potential for reusing the analysis patterns.

A KNOWLEDGE-BASED APPROACH TO COOPERATIVE RELATIONAL DATABASE QUERYING

We present in this paper an approach to providing cooperativeness in database querying using artificial intelligence techniques. The main focus is a cooperative interface that assists nonexperienced and casual users in extracting useful answers from a relational database. Our approach relies on an architecture that comprises two knowledge bases which store rules that describe the application domain and guide the process of query formulation and answering. A subset of SQL is used for expressing queries, and the cooperative interface relieves the user from knowing its full syntax and the structure of the database.

Cooperative querying in relational databases

This paper proposes an architecture for cooperative access to databases, and describes a cooperative interface for querying relational databases (i.e., an interface that assists non-experienced and casual users to get useful answers from a relational database) which has been implemented based on this architecture. The architecture comprises two knowledge bases which store rules that describe the application domain and that guide the process of query formulation and answering. Queries are formulated using a subset of SQL which does not require the users knowledge of the language syntax and of the database structure. The interface uses the rules stored in the knowledge bases to expand the queries and to provide more relevant answers to the user.

Identification of aspect candidates by inspecting use cases descriptions

The identification of aspect candidates in the early phases of software development process allows improving the modularization of the requirements, to detect conflicts early and maintain the separation of concerns in the later phases of the process. However, the identification of aspects in initial phases is a non-trivial task, which can be costly and too time-consuming. This paper presents a technique to assist in the identification of aspect candidates in the requirements, through inspection. Two case studies were carried out to evaluate the proposed technique. The results obtained from the case studies let us conclude that the proposed technique facilitates the identification of aspect candidates.

Analysis Procedure for Validation of Domain Class Diagrams Based on Ontological Analysis

A well-conceived conceptual model is essential to obtain systems that are easier to maintain. The UML class diagram is a powerful tool that can be applied at this step, but the developer has to have a clear understanding of the domain concepts in order to yield a diagram that captures the concepts and the relations of the domain. In order to verify the adequacy of the class diagram, an analysis of the objects essence and its permitted relations can be accomplished. This analysis is called ontological analysis, but its execution can be quite difficult because it is necessary to master sophisticated philosophical concepts like identity and rigidity. This article presents a procedure that aims to accomplish the ontological analysis of the UML class diagram without exposing the complexity of the concepts that underlies the procedure.

Internet economic news gathering and classification: a neural network software agent based approach

The explosive growth on the amount of information available on the Internet makes it a hard task to select what is worth reading in our scarce available time. The project described in this paper tackles this problem using techniques taken from the areas of autonomous agents and artificial neural networks. An agent for economic news gathering and classification was designed implemented and successfully tested over sites about economy available on the Internet. Inputs to the system are news text picked up by software agents from selected Internet economic sites, and the outputs are those same news classified by topics of interest in at most six classes. The classification is based on an artificial neural network that runs inside the classification agent, especially trained with patterns that allow carrying out the desired news analysis and classifications.

Cataloguing provenance-awareness with patterns

This paper explores the organization of provenance as a catalogue of non-functional requirement. The aim of this paper is to introduce a systematic approach to design a provenance catalogue for reuse using consolidated software engineering techniques. Provenance captures a derivation history of data products and is essential to the long-term preservation, to reuse, and to determine data quality. We propose the provenance catalogue that took into account NFR patterns and provenance taxonomies and specifications to define its softgoals. This work depicts a novel approach on provenance describing it as a Softgoal Interdependency Graph, a reusable framework that makes explicit characterization, decomposition, relationships and operationalization of elements that can be satisfied with the software. We exemplify the approach in a real usage scenario based on scientific software development.