Empirical review of Java program repair tools: a large-scale experiment on 2,141 bugs and 23,551 repair attempts

Assoc.-Prof. Rui Abreu (Universität Lissabon) | 19.12.2019 | 10:00 Uhr | S.1.37

Abstract:

In the past decade, research on test-suite-based automatic program repair has grown significantly. Each year, new approaches and implementations are featured in major software engineering venues. However, most of those approaches are evaluated on a single benchmark of bugs, which are also rarely reproduced by other researchers. In this paper, we present a large-scale experiment using 11 Java test-suite-based repair tools and 2,141 bugs from 5 benchmarks. Our goal is to have a better understanding of the current state of automatic program repair tools on a large diversity of benchmarks. Our investigation is guided by the hypothesis that the repairability of repair tools might not be generalized across different benchmarks. We found that the 11 tools 1) are able to generate patches for 21% of the bugs from the 5 benchmarks, and 2) have better performance on Defects4J compared to other benchmarks, by generating patches for 47% of the bugs from Defects4J compared to 10-30% of bugs from the other benchmarks. Our experiment comprises 23,551 repair attempts, which we used to find causes of non-patch generation. These causes are reported in this presentation, which can help repair tool designers to improve their approaches and tools. This work was presented at ESEC/FSE19 and was given an ACM SIGSOFT Distinguished Paper Award.

CV:

Rui Abreu holds a Ph.D. in Computer Science – Software Engineering from TU Delft, The Netherlands, and a M.Sc. in Computer and Systems Engineering from the U.Minho, Portugal. His research revolves around software quality, with emphasis on automating the testing and debugging phases of the software development life-cycle as well as self-adaptation. He is the recipient of 7 Best Paper Awards (including a 2019 FSE  Distinguished Paper Award). Before joining IST, U.Lisbon as an Associate Professor (with habilitation), he was a member of the Model-Based Reasoning group at PARC’s System and Sciences Laboratory. He has co-founded DashDash in January 2017, a platform to create web apps using only spreadsheet skills. Currently, he is enjoying a sabbatical leave as a Visiting Scientist at Google NY’s. He is also passionate about soccer and a FC Porto fan.

 

 

 

 

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Applications and Challenges of Sentiment and Stance Analysis

Dr. Petra Kralj Novak | December 9, 2019 | 16:00 | S.2.42

Abstract:

Social media are computer-based technologies that provide means of information and idea sharing, as well as entertainment and engagement handly available as mobile applications and websites to both private users and businesses.  As social media communication is mostly informal, it is an ideal environment for the use of emoji and for detecting the population’s sentiments and stance. Sentiment* and stance** analysis have been heavily researched in the last decade and the technology to address these data analysis tasks have developed rapidly. In this talk, several inspiring sentiment and stance analysis applications will be presented, varying in data source, topics, language, and approaches used. As a result of several years of experience in sentiment and stance analysis, best practices guidelines will be provided and remaining challenges exposed.
*Sentiment analysis is the field of study that analyzes people’s opinions, sentiments, evaluations, attitudes, and emotions from a text.
**Stance analysis is the task of automatically determining from text whether the author of the text is in favor of, against, or neutral towards a proposition or target.

CV:

Dr. Petra Kralj Novak is a researcher at the Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia. Her research belongs to the wide area of knowledge discovery from databases. Currently, as a postdoctoral researcher, she analyses social and mainstream media focusing on the mediated sentiment and hate speech. Avant-garde research in analyzing the role of emojis in conveying sentiment was published in P. Kralj Novak, et al. „Sentiment of emojis“ and is the main reference for current research in the analysis of emoji. Dr. Kralj Novak publishes research papers and datasets in top academic venues. Her thesis focused on rule induction from class labeled data, where the induced rules are intended for human interpretation. The main findings of the thesis are published in Journal of Machine Learning Research and in the Encyclopedia of Machine Learning. She also designed and implemented GMOtreck – a system for optimization of laboratory level traceability of genetically modified organisms. Dr. Kralj Novak regularly serves in scientific programs of major academic and industrial conferences such as ICDM, ICML, DS, IDA, and Southern Data Science. She is PC chair of the  22nd International Conference on Discovery Science (2019, Split Croatia). From 2006 to 2009, and from 2018 on she is secretary and treasurer of SLAIS – the Slovenian Artificial Intelligence Society. She has also actively collaborated in several national and European research projects. She is the coordinator of the EU REC AG project IMSyPP: Innovative Monitoring Systems and Prevention Policies of Online Hate Speech (2020-2022).

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Connecting Trust – decentralization of the internet

Assoc.-Prof. Dr. Antorweep Chakravorty | November 25, 2019 | 16:00 | S.2.42

Abstract:

Blockchain is an innovation for creating distributed trust between users facilitating the exchange of value over a network. It can be seen as a decentralized read-only database operated collectively by participants in the network. Participants in the network can be different organizations that provide computing infrastructure to maintain a single version of a decentralized ledger. Each participant locally maintain the same version of this ledger in their own environment and agree upon any updates or changes to its state by employing some consensus algorithms. This enables the trust to be distributed throughout the network, without the need for a central intermediary. The decentralization of trust allows the blockchain technology to be transparent, secure, auditable, redundant and immutable. Since each participant maintains the same version of the truth, it removes the potential of conflict. Additionally, it also enhances the trust of end-users using applications provided by organizations driven by blockchains as they are able to get confirmation about operations on their data from multiple distinct entities rather than a single centralized party. These features of the blockchain has lead to its adoption not only in financial sectors but also in health, energy, IoT, supply chain and smart cities.

 

CV:

Dr. Antorweep Chakravorty is an Associate Professor at the University of Stavanger. His current research and development work is in the field of applied Blockchains, Big Data, Large Scale Machine Learning and Data Privacy. He has an interest in real-world problems, especially development of privacy enabled data-driven services in smart energy, healthcare and smart city domains. Antorweep completed his PhD. in 2015 with a thesis on Privacy Preserving Big Data Analytics at the University of Stavanger, Norway. Along with having a background in applied research in data-driven solutions, he is also involved in mentoring, teaching and supervision. He spent 6 months on a research exchange program at IBM Thomas J. Watson Research Center, New York, USA.

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Enhancing Context Knowledge Repositories with Justifiable Exceptions

Prof. Dr. Thomas Eiter | October 25, 2019 | 14:00 | S.2.69

Abstract:

The Contextualized Knowledge Repository (CKR) framework was conceived as a logic-based approach for representing context dependent knowledge, which is a well-known area of study in AI, based on description logics. The framework has a two-layer structure with a global context that contains context-independent knowledge and meta-information about the contexts, and a set of local contexts with specific knowledge bases.  In many practical cases, it is desirable that inherited global knowledge can be „overridden“ at the local level. In order to address this need, an extension of CKR with global defeasible axioms was developed: these axioms locally apply to individuals unless an exception for overriding exists; such an exception, however, requires a justification that is provable from the knowledge base.

The formalization of this intuition has some desirable semantic properties, and furthermore allows for a translation of reasoning tasks on extended CKRs to datalog programs under the answer set (i.e., stable) semantics. This work complements other work on nonmonotonic extensions of description logics with an expressive formalism for exception handling by overriding, and adds to the body of results on using deductive database technology in these areas.

This is joint work with Loris Bozzato and Luciano Serafini (Fondazione Bruno Kessler, Trento).

CV:

Thomas Eiter is a full professor in the Faculty of Informatics at Vienna University of Technology (TU Wien), Austria, and Head of the Institute of Information Systems, where he also leads the Knowledge Based Systems Group. From 1996-1998, he was an associate professor of Computer Science at the University of Giessen, Germany.

Prof. Eiter’s current research interests include knowledge representation and reasoning, computational logic, foundations of information systems, and complexity in AI.  He has contributed to the DLV system and some of its extensions, e.g. the DLVHEX system. He has been involved in various national and international research and training projects, and he has been serving on a number of professional committees and boards. Prof. Eiter’s work has been honored with some best paper awards; he is a Fellow of the  European Association for Artificial Intelligence (EurAI), a Member of the Academia Europea, and a Corresponding Member of the Austrian Academy of Sciences.

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Extracting extreme aspects from time series with applications

Prof. Milan Stehlík | October 18, 2019 | 15:00 | V.1.27

Abstract:

Extracting chaotical and stochastic parts of information from time series needs very specific techniques. Motivated by two applications, image processing for cancer discrimination and methane emissions modelling we will explain the necessary techniques for statistical learning on chaotical and stochastic parts from data. In particular, Tsallis Entropy will be introduced and its role in information theory for dynamical system explained. Iterated function systems will be used as an example for chaos re-simulation. Construction of stochastic fractals will be discussed. We will show the importance of decomposition of data to stochastic, deterministic and chaotic part.

CV:

Professor Milan Stehlík  obtained his PhD in 2003 at Comenius University, Bratislava,  Slovakia,  and he habilitated in Statistics in 2011 at Johannes Kepler University in Linz, Austria. During 1.3.2014-1.10.2015 he was Associate Professor at Universidad Técnica Federico Santa María, Chile. In 2015 he received Full Professorship at University of Valparaiso, Valparaiso, Chile.

Currently he is Visiting professor at the Department of Statistics & Actuarial Science, The University of Iowa. In 2018 he was visiting Full Professor at School of Mathematics & Statistical Sciences Arizona State University, AZ, USA. He was involved in several international projects and collaborations in Austria, Spain, Russia, Canada, Germany, USA among others.

He does research in Extremes, Optimal design of experiments, Statistical Modelling, Neural Computing, Cancer discrimination. He servers as Associate Editor for Europe of Neural Computing and Applications, Associate Editor of Journal of Applied Statistics and Revstat.  He has been Principal Investigator of Innovative project LIT-2016-1-SEE-023 Title: Modeling complex dependencies: how to make strategic multicriterial decisions?  at Linz Institute of Technology, Austria and Chilean FONDECYT Regular. He published more than 180 papers and gave more than 190 talks.

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