2022
Authors
Mariano, A; Cabeleira, F; Santos, LP; Falcão, G;
Publication
Cybersecurity and High-Performance Computing Environments
Abstract
2022
Authors
Pereira, RB; Ferreira, JF; Mendes, A; Abreu, R;
Publication
9TH IEEE/ACM INTERNATIONAL CONFERENCE ON MOBILE SOFTWARE ENGINEERING AND SYSTEMS, MOBILESOFT 2022
Abstract
When developing mobile applications, developers often have to decide when to acquire and when to release resources. This leads to resource leaks, a kind of bug where a resource is acquired but never released. This is a common problem in Android applications that can degrade energy efficiency and, in some cases, can cause resources to not function properly. In this paper, we present an extension of EcoAndroid, an Android Studio plugin that improves the energy efficiency of Android applications, with an inter-procedural static analysis that detects resource leaks. Our analysis is implemented using Soot, FlowDroid, and Heros, which provide a static-analysis environment capable of processing Android applications and performing inter-procedural analysis with the IFDS framework. It currently supports the detection of leaks related to four Android resources: Cursor, SQLite-Database, Wakelock, and Camera. We evaluated our tool with the DroidLeaks benchmark and compared it with 8 other resource leak detectors. We obtained a precision of 72.5% and a recall of 83.2%. Our tool was able to uncover 191 previously unidentified leaks in this benchmark. These results show that our analysis can help developers identify resource leaks.
2022
Authors
Brito, C; Esteves, M; Peixoto, H; Abelha, A; Machado, J;
Publication
WIRELESS NETWORKS
Abstract
Continuous ambulatory peritoneal dialysis (CAPD) is a treatment used by patients in the end-stage of chronic kidney diseases. Those patients need to be monitored using blood tests and those tests can present some patterns or correlations. It could be meaningful to apply data mining (DM) to the data collected from those tests. To discover patterns from meaningless data, it becomes crucial to use DM techniques. DM is an emerging field that is currently being used in machine learning to train machines to later aid health professionals in their decision-making process. The classification process can found patterns useful to understand the patients' health development and to medically act according to such results. Thus, this study focuses on testing a set of DM algorithms that may help in classifying the values of serum creatinine in patients undergoing CAPD procedures. Therefore, it is intended to classify the values of serum creatinine according to assigned quartiles. The better results obtained were highly satisfactory, reaching accuracy rate values of approximately 95%, and low relative absolute error values.
2022
Authors
Ribeiro, DP; Anjo, A; Henriques, PR;
Publication
International Conference on Applied Computing 2022 and WWW/Internet 2022
Abstract
The existence of internal helpdesk teams is a common occurrence in companies nowadays, especially when considering the IT sector. These teams are an expensive resource and are only able to serve a limited number of users at a given moment, which evidences the importance of helpdesk teams operating as efficiently as possible. A common occurrence in the daily operations of these teams consists in the existence of a set of repeated tasks that could be automated through the usage of a chatbot capable of acting on behalf of helpdesk team members. By allowing a chatbot to perform some of these repeated actions, helpdesk teams are able to focus on other tasks, thus allowing to increase their productivity. Additionally, the usage of chatbots to assist a helpdesk team creates a highly available tool, capable of giving answers in a short time frame. In this paper, the design and implementation of such a tool is presented, including concepts and approaches related to chatbot development. As a result, a fully functional chatbot named Triton was produced, capable of helping employees of a consulting company with helpdesk-related problems and questions. Copyright © (2022) by International Association for Development of the Information Society (IADIS). All rights reserved.
2021
Authors
Esteves, T; Neves, F; Oliveira, R; Paulo, J;
Publication
Middleware
Abstract
2021
Authors
Alam, MI; Halder, R; Pinto, JS;
Publication
JOURNAL OF SYSTEMS AND SOFTWARE
Abstract
Deductive verification has gained paramount attention from both academia and industry. Although intensive research in this direction covers almost all mainstream languages, the research community has paid little attention to the verification of database applications. This paper proposes a comprehensive set of Verification Conditions (VCs) generation techniques from database programs, adapting Symbolic Execution, Conditional Normal Form, and Weakest Precondition. The validity checking of the generated VCs for a database program determines its correctness w.r.t. the annotated database properties. The developed prototype DBverify based on our theoretical foundation allows us to instantiate VC generation from PL/SQL codes, yielding to detailed performance analysis of the three approaches under different circumstances. With respect to the literature, the proposed approach shows its competence to support crucial SQL features (aggregate functions, nested queries, NULL values, and set operations) and the embedding of SQL codes within a host imperative language. For the chosen set of benchmark PL/SQL codes annotated with relevant properties of interest, our experiment shows that only 38% of procedures are correct, while 62% violate either all or part of the annotated properties. The primary cause for the latter case is mostly due to the acceptance of runtime inputs in SQL statements without proper checking.
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