Manoel Gomes de Mendonça Neto

A Contextualized Vocabulary Model for identifying technical debt on code comments

The identification of technical debt (TD) is an important step to effectively manage it. In this context, a set of indicators has been used by automated approaches to identify TD items, but some debt may not be directly identified using only metrics collected from the source code. In this work we propose CVM-TD, a model to support the identification of technical debt through code comment analysis. We performed an exploratory study on two large open sources projects with the goal of characterizing the feasibility of the proposed model to support the detection of TD through code comments analysis. The results indicate that (1) developers use the dimensions considered by CVM-TD when writing code comments, (2) CVM-TD provides a vocabulary that may be used to detect TD items, and (3) the proposed model needs to be calibrated in order to reduce the difference between comments returned by the vocabulary and those that may indicate a TD item. Code comments analysis can be used to detect TD in software projects and CVM-TD may support the development team to perform this task.

SourceMiner: Towards an Extensible Multi-perspective Software Visualization Environment

In spite of the available resources provided by modern IDEs, program understanding remains as a difficult task in software engineering. This paper presents a software visualization environment named SourceMiner. Implemented as an Eclipse plug-in to enhance software comprehension activities, SourceMiner is an extensible, interactive and coordinated multi-perspective environment. This paper describes the principles behind the design of SourceMiner, and discusses how it has been used to support software comprehension activities such as the identification of code smells and the characterization of object-oriented software systems.

A tertiary study on technical debt: Types, management strategies, research trends, and base information for practitioners

Abstract Context The concept of technical debt (TD) contextualizes problems faced during software evolution considering the tasks that are not carried out adequately during its development. Currently, it is common to associate any impediment related to the software product and its development process to the definition of TD. This can bring confusion and ambiguity in the use of the term. Besides, due to the increasing amount of work in the area, it is difficult to have a comprehensive view of the plethora of proposals on TD management. Objective This paper intends to investigate the current state of research on TD by identifying what research topics have been considered, organizing research directions and practical knowledge that has already been defined, identifying the known types of TD, and organizing what activities, strategies and tools have been proposed to support the management of TD. Method A tertiary study was performed based on a set of five research questions. In total, 13 secondary studies, dated from 2012 to March 2018, were evaluated. Results The results of this tertiary study are beneficial for both practitioners and researchers. We evolved a taxonomy of TD types, identified a list of situations in which debt items can be found in software projects, and organized a map representing the state of the art of activities, strategies and tools to support TD management. Besides, we also summarized some research directions and practical knowledge, and identified the research topics that have been more considered in secondary studies. Conclusion This tertiary study revisited the TD landscape. Its results can help to identify points that still require further investigation in TD research.

A Study on Identification of Documentation and Requirement Technical Debt through Code Comment Analysis

Context: The TD concept reflects the challenging decisions that developers and managers need to take to achieve short-term benefits to keep the customers satisfied and to survive in a competitive market. The identification of technical debt (TD) is an important step to effectively manage TD items and make TD manageable and explicit to keep the amount of TD under control. Researchers have developed automated approaches to identify TD items using indicators derived from source code metrics. However, those indicators do not always point to TD that developer teams consider real problems and cannot identify many types of relevant TD. Objective: This work seeks to identify comment patterns and their relationships which can support the identification process of documentation and requirement debts. Method: We performed a qualitative and quantitative analysis to investigate acceptable patterns of comments which indicate the existence of documentation and requirement debts. Results: We classify factors which can impact on the detection automated of documentation and requirement debts. Besides, the performed study provided a set of new patterns to detect documentation and requirement debts. Conclusion: This research contributes to bridge the gap between the TD identification area and code comment analysis, successfully using code comments to detect documentation and requirement debts.

A Strategy Based on Multiple Decision Criteria to Support Technical Debt Management

Technical debt (TD) refers to likely long-term costs associated with software development shortcuts taken by programmers to achieve short-term business benefits. If a development team does not manage TD, it can cause significant long-term problems such as high maintenance costs. Management strategies monitor debt items and evaluate when and if they should be paid. In order to effectively support this task, the systematic use of decision criteria can be decisive. In this context, this paper presents a strategy for TD management that uses multiple decision criteria to decide when to pay debt items off. In addition, it presents a case study that assessed the feasibility of the proposed strategy regarding its usefulness, ease of use and self-predicted future use. The results provided positive evidence on the use of the proposed strategy, indicating (i) that it can be useful in supporting TD management activities and (ii) that it can bring gains in terms of productivity, performance, and efficacy when evaluating the desirable time to pay debt items off.

An Approach for Semantically-Enriched Recommendation of Refactorings Based on the Incidence of Code Smells.

Code smells are symptoms of bad decisions on the design and development of software. The occurrence of code smells in software can lead to costly consequences. Refactorings are considered adequate resources when it comes to reducing or removing the undesirable effects of smells in software. Ontologies and semantics can play a substantial role in reducing the interpretation burden of software engineers as they have to decide about adequate refactorings to mitigate the impact of smells. However, related work has given little attention to associating the recommendation of refactorings with the use of ontologies and semantics. Developers can benefit from the combination of code smells detection with a semantically-oriented approach for recommendation of refactorings. To make this possible, we expand the application of our previous ontology, ONTOlogy for Code smEll ANalysis (ONTOCEAN), to combine it with a new one, Ontology for SOftware REfactoring (OSORE). We also introduce a new tool, our REfactoring REcommender SYStem (RESYS) which is capable of binding our two ontologies. As a result, refactorings are automatically chosen and semantically linked to their respective code smells. We also conducted a preliminary evaluation of our approach in a real usage scenario with four open-source software projects.

An Ontology-based Approach to Analyzing the Occurrence of Code Smells in Software.

Code Smells indicate potential flaws in software design that can lead to costly consequences. To mitigate the bad effects of Code Smells, it is necessary to detect and fix defective code. Programmatic processing of Code Smells is not new. Previous works have focused on detection and representation to support the analysis of faulty software. However, such works are based on a syntactic operation, without taking advantage on semantic properties of the software. On the other hand, there are several ways to provide semantic support in software development as a whole. Ontologies, for example, have recently been usedl. The application of ontologies for inferring semantic mechanisms to aid software engineers in dealing with smells may be of great value. As little attention has been given to this, we propose an ontology-based approach to analyze the occurrence of Code Smells in software projects. First, we present a comprehensive ontology that is capable of representing Code Smells and their association with software projects. We also introduce a tool that can manipulate our ontology in order to provide processing of Code Smells as it mines software source-code. Finally, we conducted an initial evaluation of our approach in a real usage scenario with two large open-source software repositories.