Marco Antônio Pereira Araújo

AD-Reputation: A Reputation-Based Approach to Support Effort Estimation

Estimating the effort on software maintenance activities is a complex task. When inaccurately accomplished, effort estimation can reduce the quality and hinder software delivery. In a scenario, in which the maintenance and evolution activities are geographically distributed, collaboration is a key issue to estimate and meet deadlines. In this vein, dealing with reputation of developers, as well as establish and promote trust among them, are factors that affect collaboration activities. This paper presents an approach aimed to support effort estimation on collaborative maintenance and evolution activities. It encompasses a model for reputation calculation, visualization elements and the integration with change request repositories. Through an experimental study, quantitative and qualitative data was collected. A statistical analysis was applied and shown that the AD-Reputation is feasible to estimate the effort spent on collaborative maintenance activities.

Investigating Reputation in Collaborative Software Maintenance: A Study Based on Systematic Mapping

[Background] Reputation systems have attracted the attention of researchers when it comes to collaborative systems. In the context of collaborative software maintenance, systems of this type are employed to facilitate the collection, aggregation and distribution of reputation information about a participant. GiveMe Infra is an infrastructure that supports collaborative software maintenance performed both by co-located and geographically distributed teams. In this last case, reputation is one of the factors that influence collaboration. Despite this recognized relevance, to the best of our knowledge, there is a shortage of tools providing reputation functionalities in the context of collaborative software maintenance. [Objective] However, GiveMe Infra needs to identify and correlate metrics, measures, criteria and factors (called parameters) that are used in defining the value of reputation of an entity. These parameters, used to determine the degree of reputation, can provide evidence of parameters to be used in the context of software maintenance and evolution. [Method] In order to achieve this goal, a systematic mapping was performed. Both the established protocol and the process adopted during the mapping are shown in this article. The parameters identified, as well as how to apply them in the context of software maintenance are demonstrated through an analysis scenario. [Results] Our goal has been achieved since the systematic mapping allowed the identification of the parameters used in defining reputation and even parameters that would not allow a correlation with the context of collaborative software maintenance. The contribution of this research is threefold, in its investigation carried out, the list of identified parameters and also the application in the collaborative software maintenance context.

HEAL ME: an architecture for health software ecosystem evaluation

Software Ecosystems are complex environments. Several actors are grouped on a software platform, facilitating investments in research, development, and reuse of components. However, such investments may be lost if the ecosystem is not solid. Therefore, it is important to assess the health of an ecosystem. This work presents HEAL-ME architecture, which main goal is to evaluate the health of software ecosystems. A pilot case study using a scientific software ecosystem is also presented. Enhancements will be provided for a complete SECO health assessment.

A collaborative approach to support e-science activities

In recent years the scientific research has undergone substantial changes. In particular, there is a greater collaboration between research groups, which leads to an increase in the use of information processing techniques, and, therefore, the need to share results and observations among participants of a research. This work has as main goal to propose an architecture to support distributed processing of scientific experiments, as an implementation of so-called collaborative laboratories.

GiveMe Trace: A Software Evolution Traceability Support Tool

Traceability is a key factor in the analysis of the changes that software undergoes throughout its evolution. The main purpose of analysis is to minimize the side effects of these changes and, when it is made to the source at a lower level of abstraction (methods) and in an integrated manner, it can provide more accurate data in order to support decision making. This article presents the GiveMe Trace tool, integrated with a multiple view interactive environment that, among other features, can generate information about the traceability between source code and artifacts its different versions. This information is based on software versions analysis from software repository. As a result, occurrences of changes in classes or methods are shown. A proof of concept was carried out through which repositories versions of two distinct real projects were analyzed. At the end, it was possible to obtain evidences on the feasibility of the use of GiveMe Trace to support traceability between the source code and versions.

Code Smell Analyzer: A Tool To Teaching Support Of Refactoring Techniques Source Code

The present paper addresses the refactoring techniques defended by Martin Fowler (2002), and bad smells present in your work. A tool was developed to support teaching and assessing academic works called Code Smell Analyzer, which aims to identify bad smells codes, presenting how they can be corrected and avoided using refactoring techniques. The tool works integrated into the Eclipse IDE (Integrated Development Environment) and aims to assist in the teaching and assessment of academic works about bad smells and refactoring of source code.

An Approach for Detecting Unnecessary Cyclomatic Complexity on Source Code

Seeking products quality is essential nowadays. One of the many quality aspects in software development is the source code complexity. Not paying attention to the complexity during the development can result in unexpected cost, caused by the difficulty on the source code understanding. The goal of this paper is to introduce an initial approach to identify unnecessary complexity in source code. Besides identifying, the approach can also show to its user how to properly rewrite the source code without the unnecessary complexity. The approach is based on the static analysis of the source code control flow graph. Once the unnecessary complexity is identified, the graph is refactored in order to allow the user to understand the improvement on the source code. The approach was included in a software tool in order to prove its concept. A performance evaluation of the approach was performed, resulting in a high accuracy. Two experimental studies were also performed to assess the viability of the approach when used by real users. The evidences provided by these studies suggests that the approach support the unnecessary complexity removal.

Using Context Elements and Data Provenance to Support Reuse in Scientific Software Ecosystem Platform.

[Background] Managing contextual elements and provenance information plays a key role in the context of scientific experiments. Currently the scientific experimentation process requires support for collaborative and distributed activities. Detailed logging of the steps to produce results, as well as the environment context information could allow scientists to reuse these results in future experiments and reuse the experiment or parts of it in another context. [Objectives] The goal of this paper is to present a provenance and context metadata management approach that support researchers to reuse experiments in a collaborative and distributed platform. [Method] First, the context and provenance management life cycle phases were analyzed, considering existing models. Then it was proposed a conceptual framework to support the analysis of contextual elements and provenance data of scientific experiments. An ontology capable of extracting implicit knowledge in this domain was specified. This approach was implemented in a scientific ecosystem platform. [Results] An initial evaluation shown evidences that this architecture is able to help researchers during the reuse and reproduction of scientific experiments. [Conclusions] Context elements and data provenance, associated with inference mechanisms, can be used to support the reuse in scientific experimentation process.

Health Evaluation in Software Ecosystems.

Context: The quality of a Software Ecosystem (SECO) platform and its available products are important characteristics to ensure its success. However, this concept goes beyond the traditional approaches of quality assurance, including concepts such as SECO ́s health. Objectives: The aim of this study is propose an evaluation process to application of health metrics. In addition, this metrics were formalized to make feasible your application and improve the obtained results. Method: A systematic mapping was conducted with the aim of analyzing the SECO quality research area, highlighting the state of the art and identifying its main characteristics. In addition, the main approaches and metrics present in the literature for SECO quality and health evaluation are detailed. This work presents an observational study used to define relevant heath metrics considering an evaluation process. Results: The metrics were formalized and evaluated by specialists. A health evaluation process was developed to applicate this metrics. This process is supported by an architecture named

Test case prioritization: a systematic review and mapping of the literature

Test case prioritization (TCP) techniques aim to reorder test cases execution according to a goal. One common goal is fault detection, in which test cases that have a higher chance of detecting a fault are executed first than the remaining test cases. The goal of this study is to investigate TCP empirical studies in order to synthesize reported effectiveness results and provide a basis for future research. We conducted a systematic literature mapping to characterize TCP empirical studies and a systematic literature review to analyze reported TCP techniques effectiveness results. Among selected studies from 1999 to 2016, we found that there is a high amount of empirical studies evaluating many TCP techniques. However, when we applied our quality assessment criteria, most of them were discarded, indicating that they might have methodological problems. Analyzed studies reported results of coverage-based TCP techniques. Furthermore, we found that some context factors regarding faults, test cases of the application being tested and coverage granularity considered by TCP techniques may significantly affect the effectiveness of their execution. These results suggest that more rigorous empirical methodology is needed when evaluating TCP techniques and also, authors need to compare effectiveness results of their proposed TCP techniques with well established techniques to generate more evidences Furthermore, our analysis of significant factors on TCP techniques effectiveness can guide researchers when planning empirical evaluations and help them choosing features to compose new techniques.