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Aldeida AletiArtifact Chairs

Monash University

Australia

Aldeida is a Senior Lecturer at the Faculty of Information Technology, Monash University in Australia, where she leads the Software Engineering Discipline. Aldeida’s research is in the area of search-based software engineering (SBSE), with a particular focus on what makes software engineering problems (design, testing, program repair) hard to optimise and designing approaches that make it easier to apply SBSE techniques to new problems. Aldeida has published more than 50 papers at top AI, optimisation and software engineering venues, served as PC member and organising committee at both SE and optimisation/AI conferences, such as ASE, ICSE, GECCO, SSBSE, ISSTA, IJCAI, etc., is in the editorial board of JSS, and a reviewer for journals such as JSS, IEEE TSE, IEEE TEC, etc. Aldeida has attracted more than $1,000,000 in competitive research funding and was awarded the prestigious Discovery Early Career Researcher Award (DECRA fellowship) from the Australian Research Council.

Principles of Artifacts: Some Lessons Learned

This mini keynote summarizes a few properties that should be followed when publishing artifacts, explains the three levels of achievements that the artifact communities nowadays distinguish, and emphasizes the need for standardization and uniformity in the processes, such that our artifact processes and badges are understandable by others.

Dirk Beyer

LMU Munich, Germany

Germany

Dirk Beyer is Professor of Computer Science and has a Research Chair for Software Systems at LMU Munich, Germany. He was Full Professor at University of Passau (2009-2016), Assistant and Associate Professor at Simon Fraser University, B.C., Canada, and Postdoctoral Researcher at EPFL in Lausanne, Switzerland (2004-2006) and at the University of California, Berkeley, USA (2003-2004) in the group of Tom Henzinger. Dirk Beyer holds a Dipl.-Inf. degree (1998) and a Dr. rer. nat. degree (2002) in Computer Science from the Brandenburg University of Technology in Cottbus, Germany. In 1998 he was Software Engineer with Siemens AG, SBS Dept. Major Projects in Dresden, Germany. His research focuses on models, algorithms, and tools for the construction and analysis of reliable software systems. He is the architect, designer, and implementor of several successful tools. For example, CrocoPat is the first efficient interpreter for relational programming, CCVisu is a successful tool for visual clustering, and CPAchecker and BLAST are two well-known and successful software model checkers.

Reasoning on immutability is important for preventing bugs, e.g., in multi-threaded software. So far, static analysis to infer immutability properties has mostly focused on individual objects and references. Reasoning about fields and entire classes, while significantly simpler, has gained less attention. Even a consistently used terminology is missing, which makes it difficult to implement analyses that rely on immutability information. We propose a model for class and field immutability that unifies terminology for immutability flavors considered by previous work and covers new levels of immutability to handle lazy initialization and immutability dependent on generic type parameters. We implement CiFi, a set of modular, collaborating analyses for different flavors of immutability, inferring the properties defined in our model and propose a benchmark of representative test cases for class and field immutability. We use the benchmark to showcase CiFi’s precision and recall, in comparison to state of the art, and use CiFi to study the prevalence of immutability in real-world libraries, showcasing the practical quality and relevance of our model.

Tobias Roth

Technische Universität Darmstadt

Dominik Helm

Technische Universität Darmstadt

Michael Reif

Technische Universität Darmstadt

Mira Mezini

Technische Universität Darmstadt

Germany

Question Answering (QA) is an attractive and challenging area in NLP community. There are diverse algorithms being proposed and various benchmark datasets with different topics and task formats being constructed. QA software has also been widely used in daily human life now. However, current QA software is mainly tested in a reference-based paradigm, in which the expected outputs (labels) of test cases need to be annotated with much human effort before testing. As a result, neither the just-in-time test during usage nor the extensible test on massive unlabeled real-life data is feasible, which keeps the current testing of QA software from being flexible and sufficient. In this paper, we propose a method, QAAskeR, with three novel Metamorphic Relations for testing QA software. QAAskeR does not require the annotated labels but tests QA software by checking its behaviors on multiple recursively asked questions that are related to the same knowledge. Experimental results show that QAAskeR can reveal violations at over 80% of valid cases without using any pre-annotated labels. Diverse answering issues, especially the limited generalization on question types across datasets, are revealed on a state-of-the-art QA algorithm.

Songqiang Chen

School of Computer Science, Wuhan University

China

Shuo Jin

School of Computer Science, Wuhan University

China

Xiaoyuan Xie

School of Computer Science, Wuhan University, China

China

Data scientists typically practice exploratory programming using computational notebooks, to comprehend new data and extract insights. To do this they iteratively refine their code, actively trying to re-use and re-purpose solutions created by other data scientists, in real time. However, recent studies have shown that a vast majority of publicly available notebooks cannot be executed out of the box. One of the prominent reasons is the deprecation of data science APIs used in such notebooks, due to the rapid evolution of data science libraries. In this work we propose RELANCER, an automatic technique that restores the executability of broken Jupyter Notebooks, in near real time, by upgrading deprecated APIs. RELANCER employs an iterative runtime error driven approach to identify and fix one API issue at a time. This is supported by a machine-learned model which uses the runtime error message to predict the kind of API repair needed - an update in API or package name, a parameter, or a parameter value. Then RELANCER creates a search space of candidate repairs by combining knowledge from API migration examples on GitHub as well as the API documentation and employs a second machine learned model to rank this space of candidate mappings. An evaluation of RELANCER on a curated dataset of 255 un-executable Jupyter Notebooks from Kaggle shows that RELANCER can successfully restore the executability of 56% of the subjects, while baselines relying on just GitHub examples and just API documentation can only fix 37% and 36% of the subjects respectively. Further, pursuant to its real-time use case, RELANCER can restore execution to 49% of subjects, within a 5 minute time limit, while a baseline lacking its machine learning models can only fix 24%.

Chenguang Zhu

University of Texas at Austin

United States

Ripon Saha

Fujitsu Laboratories of America, Inc.

United States

Mukul Prasad

Fujitsu Research of America

United States

Sarfraz Khurshid

The University of Texas at Austin

We introduce a new approach, CONCH, for de-bloating contexts for all the object-sensitive pointer analysis algorithms developed for object-oriented languages, where the calling contexts of a method are distinguished by its receiver objects. Our key insight is to approximate a recently proposed set of two necessary conditions for an object to be context-sensitive, i.e., context-dependent (whose precise verification is undecidable) with a set of three linearly verifiable conditions (in terms of the number of statements in the program) that are almost always necessary for real-world object-oriented applications, based on three key observations regarding context-dependability for their objects used. To create a practical implementation, we introduce a new IFDS-based algorithm for reasoning about object reachability in a program. By debloating contexts for two representative object-sensitive pointer analyses applied to a set of 12 representative Java programs, CONCH can speed up the two baselines together substantially (3.1x on average with a maximum of 15.9x) and analyze 7 more programs scalably, but at only a negligible loss of precision (less than 0.1%).

Dongjie He

UNSW Sydney

Australia

Jingbo Lu

UNSW Sydney

Jingling Xue

UNSW Sydney

Australia

Markdown compilers are widely used for translating plain Markdown text into formatted text, yet they suffer from performance bugs that cause performance degradation and resource exhaustion. Currently, there is little knowledge and understanding about these performance bugs in the wild. In this work, we first conduct a comprehensive study of known performance bugs in Markdown compilers. We identify that the ways Markdown compilers handle the language’s context-sensitive features are the dominant root cause of performance bugs. To detect unknown performance bugs, we develop MdPerfFuzz, a fuzzing framework with a syntax-tree based mutation strategy to efficiently generate test cases to manifest such bugs. It equips an execution trace similarity algorithm to de-duplicate the bug reports. With MdPerfFuzz, we successfully identified 216 new performance bugs in real-world Markdown compilers and applications. Our work demonstrates that the performance bugs are a common, severe, yet previously overlooked security problem.

Penghui Li

The Chinese University of Hong Kong

China

Yinxi Liu

The Chinese University of Hong Kong

Wei Meng

Chinese University of Hong Kong

China

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Tim MenziesPresenter

North Carolina State University

United States

Full prof, ex-nurse,rocketman,taxi-driver,journalist (it all made sense at the time).

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