Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
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

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.

2022

E-MANAFA: Energy Monitoring and ANAlysis tool For Android

Authors
Rua, R; Saraiva, J;

Publication
37th IEEE/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022

Abstract

Supervised
thesis

2022

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

Author
Rui António Ramada Rua

Institution
UM

2022

On the performance of WebAssembly

Author
João Gonçalves de Macedo

Institution
UM

2022

Explaining Software Faults in Source Code

Author
Francisco José Torres Ribeiro

Institution
UM

2022

Characterizing Data Scientists in the Real World

Author
Paula Sofia da Cunha Pereira

Institution
UM

2022

Análise e Optimização da Performance de Programação Estratégica baseda em Zippers

Author
José Emanuel Silva Rodrigues

Institution
UM