Pesquisa

Parceiros: Anderson Uchôa (UFC, Brasil), Matheus Paixão (UECE, Brasil), Paulo Henrique Mendes Maia (UECE, Brasil), Jerffeson Teixeira de Souza (UECE, Brasil), Ismayle de Sousa Santos (UECE, Brasil), Allysson Allex de Paula Araújo (UFCA, Brasil), Thiago do Nascimento Ferreira (University Of Michigan, EUA), and Chaiyong Ragkhitwetsagul (Mahidol University, Thailand).

Currently, digital technologies are used for several aspects of an individual's life, such as entertainment, work etc. Hence, the software industry grows every year with a constant increase in the number of products being developed. As a sub-area of Software Engineering, Software Maintenance refers to updating software after it has been deployed to its users. Among the maintenance tasks, one may mention bug fixing, source code improvement, technical debt management etc. As pointed out by Lehman, software needs to constantly change to remain useful. Thus, on average, more than half of a software project's resources are spent on maintenance.

Considering the existing software industry, the massive amount of data and the complexity of modern software generates scenarios where practitioners have difficulties in performing efficient maintenance tasks. In parallel, recent advances in Artificial Intelligence (AI) have been gaining notoriety. Techniques such as natural language processing, data mining and optimization have been successfully applied to several scientific and engineering areas. Thus, this project aims at investigating and using AI techniques to assist practitioners in software maintenance tasks. Data mining will be used to analyze large amounts of maintenance data to identify recurrent patterns and find best practices. We plan to employ natural language processing to assist in tasks related to text writing and comprehension, such as technical debt management and code review, for instance.

Parceiros: Anderson Uchôa (UFC, Brasil), Rafael Maiani de Mello (UFRJ, Brasil), Matheus Paixão (UECE, Brasil), Paulo Henrique Mendes Maia (UECE, Brasil), Carla Ilane (UFC, Brasil), and Alessandro Garcia (PUC-Rio, Brasil).

The State of Ceará is responsible for a significant portion of software development, accounting for about 20% of national production. Many of these software programs become legacy systems: they provide essential functionalities for organizations but use commonly outdated technologies. There is a great difficulty in maintaining or even modernizing these software programs, especially due to structural degradation that affects the code. However, several of these software programs are too important to be discarded. For organizations to remain competitive in national and international markets, it is crucial to modernize legacy software and use good software development practices.

The inclusion of new disruptive technologies, such as microservices and Big Data, helps to avoid the discontinuation of essential legacy software and offer various other opportunities for organizations. Thus, the project aims to: (i) conduct studies with the legacy systems of the Ceará industry that are undergoing modernization; (ii) investigate software development practices that assist in the identification, application, and restructuring of legacy code; (iii) propose and develop a technique that aids in the process of modernizing and evolving legacy code; and (iv) evaluate the impact on software quality after the restructuring proposed by the technique.

Parceiros: Anderson Uchôa (UFC, Brasil), and Juliana Alves Pereira (PUC-Rio, Brasil).

AI-enabled systems are composed of one or more components that learn how to perform a task from a given dataset. Similar to other complex systems, AI-enabled systems are also affected by poor code structures (e.g., code smells). In particular, the code quality of AI-enabled systems has rarely been studied. Unfortunately, little is known about the perception of developers of AI-enabled systems regarding the code smells that affect their systems. Understanding developers' perceptions is important to direct research efforts that aim to support the needs and problems of developers of AI-enabled systems.

Our goal is to obtain empirically driven actionable insights for researchers and tool builders about the level of knowledge regarding code smells, their perceived criticality, and the procedures used by developers to remove or minimize the effects of code smells in AI-enabled systems. We intend to conduct an exploratory survey with developers involved in the development of AI-enabled systems, including frameworks, systems, and machine learning libraries. We will also include developers who have used these frameworks to build their AI-enabled systems.

Parceiros: Marcos Kalinowski (PUC-Rio), Thibaut Vidal (PUC-Rio), Leonardo Murta (UFF), Leonardo Tizzei e Renato Cerqueira (IBM Research, Brasil), Gustavo Soares (Microsoft Research, EUA), Diego Cedrim (Amazon, Brasil), Myriung Kim (UCLA, EUA), Nenad Medvidovic (UCLA, EUA), and Jens Krinke (UCL, The United Kingdom).

This project aims to (i) perform studies concerning legacy systems of the industry in Rio de Janeiro, which are going through the modernization process; (ii) propose and develop a recommendation system to assist the refactoring process of legacy code; (iii) investigate optimization and recommendation techniques that allow the identification, application, and reintegration of refactoring in legacy code; (iv) evaluate the impact of software quality after the restructuring proposed by the recommender.

Parceiros: Microsoft Research (Gustavo Soares), Google (Emerson Murphy-Hill), Amazon (Diego Cedrim), IBM Research (Renato Cerqueira), NCSU (Christopher Parnin), UCLA (Myriung Kim), UCI (Andre van der Hoek), PUC-Rio (Marcos Kalinowski, Carlos J. P. Lucena), UFCG (Rohit Gheyi), UFAM (Tayana Conte), UFAL (Baldoino F. Neto, Marcio Ribeiro), and others.

This project aims to: (i) provide a conceptual framework for sequential refactoring as well as related concepts; (ii) develop a theory that explains how developers perform sequential refactoring in practice; (iii) propose heuristics for automated identification of sequential refactoring existing in a program; (iv) assess the quality impact of sequential refactoring; (v) evaluate and classify sequential refactoring as positive or negative based on their impact on structural degradation symptoms; and (vi) propose a recommendation system for sequential refactoring.

Parceiros: Federal University of Alagoas (Prof Baldoino Fonseca), Federal University of Pernambuco (Prof Leopoldo Teixeira), Pontifical Catholic University of Rio de Janeiro (Prof Alessandro Garcia) and Fundacao Oswaldo Cruz - Fiocruz (Dr Oswado Cruz), Brazil. Newcastle University project page.

Brazilian population has not responded well to the prevention programs to combat arboviral diseases, such as Zika and Dengue. Concerns with such diseases has led an overwhelming number of people to increasingly share online strategic information, including the discovery of mosquito breeding sites in public locations. The term social sensors refers to the online population that is motivated to contribute relevant information on social media channels. Recent increasing use of smartphones triggered the growing use of social networks even in poorer communities.

The project developed a platform for promoting virtual communities to prevent and combat Zika. Its core is the VazaZika application, which uses geolocation and gamification technologies for stimulating citizens to denounce and confirm Aedes breeding sites, and for updating users, in real time, about actions taken by health agents.

Parceiros: PUC-Rio (Prof. Alberto Raposo), PUC-Rio (Prof. Hugo Fuks), Federal University of Campina Grande (Prof. Rohit Gheyi), Federal University of Alagoas (Prof. Márcio Ribeiro), Federal University of Manaus (Prof. Tayana Conte), PUC-Rio (Prof. Simone Barbosa) and PUC-Rio (Prof. Clarisse Sousa).

The CARECO project aimed to develop: (i) recommendation systems to support collaborative maintenance of software systems; (ii) methods that support the evaluation of the quality of use of recommendation systems; (iii) new collaboration mechanisms integrated with development environments; (iv) application of advanced artificial intelligence and database techniques to develop recommendation systems that support collaborative maintenance of software systems, and (v) design and evaluation of recommendation systems to support the teaching and learning software maintenance.

Parceiros: Federal University of Alagoas (Prof Baldoino Fonseca), Pontifical Catholic University of Rio de Janeiro (Prof Alessandro Garcia) and Fundacao Oswaldo Cruz - Fiocruz (Dr Oswado Cruz), Brazil. UKRI project page.

Dengue is an endemic problem in many areas where public health services assistance is inefficient, and sometimes it is not even there. The Brazilian public health system cannot meet the demands of these areas due to the scarcity of resources available and the number of risk areas that requires monitoring. To make the matters worse, it is very difficult to identify and control dengue outbreaks in their initial stages.

To assist the surveillance and detection of dengue mosquito and outbreaks, we propose an integrated platform for population to act as an etymological surveillance agent. The goal is to collect and transmit geo-referenced data, providing information to assist in entomological surveillance of dengue. To accomplish the aforementioned goal, we developed (i) mobile applications to collect data, (ii) a web portal for centralizing data and (iii) social media mining to extract data and to monitor dengue outbreaks.