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About

About

João Saraiva is Professor Auxiliar at the Departmento de Informática, Universidade do Minho, Braga, Portugal, and a researcher member of HASLab/INESC TEC. He obtained a MSc degree from University do Minho in 1993 and a Ph.D. degree in Computer Science from Utrecht University in 1999. His main research contributions have been in the field of program language design and implementation, program analysis and transformation, and functional programming. He supervised 4 (FCT funded) PostDoc projects, 8 PhD projects (5 awarded and 3 running) and over 30 (Pos-Bologna) MSc thesis. He has published over 80 publications (scopus) in conferences and journals. He has served in over 60 program committees of international events, and in the evaluation committees of 5 research agencies: ANII (Uruguay), FRS-FNRS (Belgium), NWO (The Netherlands), FWF (Austria), and FCT (Portugal).

He has experience in participating and coordinating research projects in his research areas, both at national level with projects funded by FCT (projects: PURe, IVY, AMADEUS, CROSS, SSaaPP, AutoSeer, FATBIT, and GreenSwLab) and at international level with projects funded by EPSRC (UK), FLAD/NSF (USA) and by the European Union.

João Saraiva is one of the founders of the successful series of summer schools on Generative and Transformational Techniques in Software Engineering (GTTSE), which he co-organized in 2005, 2007, 2009, 2011, and 2015 (volumes 4143, 5235, 6491, and 7680 of LNCS - Tutorial by Springer-Verlag) in Braga. He was the organizing chair of ETAPS'07, The European Joint Conferences on Theory and Practice of Software, organized in Braga in 2007, and a member of its scientific committee (2007-2012).

Interest
Topics
Details

Details

  • Name

    João Alexandre Saraiva
  • Cluster

    Computer Science
  • Role

    Senior Researcher
  • Since

    01st November 2011
001
Publications

2023

Efficient Embedding of Strategic Attribute Grammars via Memoization

Authors
Macedo, JN; Rodrigues, E; Viera, M; Saraiva, J;

Publication
Proceedings of the 2023 ACM SIGPLAN International Workshop on Partial Evaluation and Program Manipulation, PEPM 2023, Boston, MA, USA, January 16-17, 2023

Abstract

2023

Exploring Data Analysis and Visualization Techniques for Project Tracking: Insights from the ITC

Authors
Barrocas, AN; da Silva, AR; Saraiva, J;

Publication
Quality of Information and Communications Technology - 16th International Conference, QUATIC 2023, Aveiro, Portugal, September 11-13, 2023, Proceedings

Abstract
Data analysis has emerged as a cornerstone in facilitating informed decision-making across myriad fields, in particular in software development and project management. This integrative practice proves instrumental in enhancing operational efficiency, cutting expenditures, mitigating potential risks, and delivering superior results, all while sustaining structured organization and robust control. This paper presents ITC, a synergistic platform architected to streamline multi-organizational and multi-workspace collaboration for project management and technical documentation. ITC serves as a powerful tool, equipping users with the capability to swiftly establish and manage workspaces and documentation, thereby fostering the derivation of invaluable insights pivotal to both technical and business-oriented decisions. ITC boasts a plethora of features, from support for a diverse range of technologies and languages, synchronization of data, and customizable templates to reusable libraries and task automation, including data extraction, validation, and document automation. This paper also delves into the predictive analytics aspect of the ITC platform. It demonstrates how ITC harnesses predictive data models, such as Random Forest Regression, to anticipate project outcomes and risks, enhancing decision-making in project management. This feature plays a critical role in the strategic allocation of resources, optimizing project timelines, and promoting overall project success. In an effort to substantiate the efficacy and usability of ITC, we have also incorporated the results and feedback garnered from a comprehensive user assessment conducted in 2022. The feedback suggests promising potential for the platform’s application, setting the stage for further development and refinement. The insights provided in this paper not only underline the successful implementation of the ITC platform but also shed light on the transformative impact of predictive analytics in information systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

Zipping Strategies and Attribute Grammars

Authors
Macedo, JN; Viera, M; Saraiva, J;

Publication
Functional and Logic Programming - 16th International Symposium, FLOPS 2022, Kyoto, Japan, May 10-12, 2022, Proceedings

Abstract
Strategic term rewriting and attribute grammars are two powerful programming techniques widely used in language engineering. The former relies on strategies (recursion schemes) to apply term rewrite rules in defining transformations, while the latter is suitable for expressing context-dependent language processing algorithms. Each of these techniques, however, is usually implemented by its own powerful and large processor system. As a result, it makes such systems harder to extend and to combine. We present the embedding of both strategic tree rewriting and attribute grammars in a zipper-based, purely functional setting. The embedding of the two techniques in the same setting has several advantages: First, we easily combine/zip attribute grammars and strategies, thus providing language engineers the best of the two worlds. Second, the combined embedding is easier to maintain and extend since it is written in a concise and uniform setting. We show the expressive power of our library in optimizing Haskell let expressions, expressing several Haskell refactorings and solving several language processing tasks for an Oberon-0 compiler. © 2022, Springer Nature Switzerland AG.

2022

Framing Program Repair as Code Completion

Authors
Ribeiro, F; Abreu, R; Saraiva, J;

Publication
INTERNATIONAL WORKSHOP ON AUTOMATED PROGRAM REPAIR (APR 2022)

Abstract
Many techniques have contributed to the advancement of automated program repair, such as: generate and validate approaches, constraint-based solvers and even neural machine translation. Simultaneously, artificial intelligence has allowed the creation of general-purpose pre-trained models that support several downstream tasks. In this paper, we describe a technique that takes advantage of a generative model - CodeGPT - to automatically repair buggy programs by making use of its code completion capabilities. We also elaborate on where to perform code completion in a buggy line and how we circumvent the open-ended nature of code generation to appropriately fit the new code in the original program. Furthermore, we validate our approach on the ManySStuBs4j dataset containing real-world open-source projects and show that our tool is able to fix 1739 programs out of 6415 - a 27% repair rate. The repaired programs range from single-line changes to multiple line modifications. In fact, our technique is able to fix programs which were missing relatively complex expressions prior to being analyzed. In the end, we present case studies that showcase different scenarios our technique was able to handle.

2022

WebAssembly versus JavaScript: Energy and Runtime Performance

Authors
De Macedo, J; Abreu, R; Pereira, R; Saraiva, J;

Publication
2022 INTERNATIONAL CONFERENCE ON ICT FOR SUSTAINABILITY (ICT4S 2022)

Abstract
The worldwide Web has dramatically evolved in recent years. Web pages are dynamic, expressed by programs written in common programming languages given rise to sophisticated Web applications. Thus, Web browsers are almost operating systems, having to interpret/compile such programs and execute them. Although JavaScript is widely used to express dynamic Web pages, it has several shortcomings and performance inefficiencies. To overcome such limitations, major IT powerhouses are developing a new portable and size/load efficient language: WebAssembly. In this paper, we conduct the first systematic study on the energy and run-time performance of WebAssembly and JavaScript on the Web. We used micro-benchmarks and also real applications in order to have more realistic results. Preliminary results show that WebAssembly, while still in its infancy, is starting to already outperform JavaScript, with much more room to grow. A statistical analysis indicates that WebAssembly produces significant performance differences compared to JavaScript. However, these differences differ between micro-benchmarks and real-world benchmarks. Our results also show that WebAssembly improved energy efficiency by 30%, on average, and showed how different WebAssembly behaviour is among three popular Web Browsers: Google Chrome, Microsoft Edge, and Mozilla Firefox. Our findings indicate that WebAssembly is faster than JavaScript and even more energy-efficient. Additionally, our benchmarking framework is also available to allow further research and replication.

Supervised
thesis

2022

Browser Energy Efficiency in Android

Author
Nelson Adriano Sequeira Gonçalves

Institution
UM

2022

On the performance of WebAssembly

Author
João Gonçalves de Macedo

Institution
UM

2022

Automatic generation of program executions

Author
José Nuno Castro de Macedo

Institution
UM

2022

Characterizing Data Scientists in the Real World

Author
Paula Sofia da Cunha Pereira

Institution
UM

2022

Green Software in the Large: Energy-driven Techniques, Tools and Repositories

Author
Rui António Ramada Rua

Institution
UM