Programs are becoming increasingly complex and typically contain an abundance of unneeded features, which could severely harm performance and security. Recently, we have witnessed a surge of debloating techniques that aim to create a reduced version of a program by eliminating the unneeded features therein. To debloat a program, most existing techniques require a usage profile of the program, typically provided as a set of inputs $I$. Unfortunately, these techniques tend to generate a reduced program that is overfitted to $I$ and thus fails to behave correctly for other inputs. To address this limitation of existing techniques, we propose DomGad, which has two main advantages over existing debloating approaches. First, it produces a reduced program that is guaranteed to work for entire subdomains, rather than for specific inputs. Second, it uses stochastic optimization to generate reduced programs that achieve a close-to-optimal trade-off between size reduction and generality (i.e., extent to which the reduced program is able to correctly handle inputs in its whole domain). To assess the effectiveness of DomGad, we applied our approach to a benchmark of ten Unix utility programs. Our results are promising, as they show that DomGad could produce debloated programs that achieve, on average, a 50% code reduction and 95% generality. Our results also show that DomGad performs well when compared with two state-of-the-art debloating approaches.

Qi Xin

Georgia Institute of Technology

United States

Myeongsoo Kim

Georgia Institute of Technology

United States

Qirun Zhang

Georgia Institute of Technology, USA

Alessandro Orso

Georgia Tech

United States

The research community has long recognized a complex interrelationship between test set size, test adequacy criteria, and test effectiveness in terms of fault detection. However, there is substantial confusion about the role and importance of controlling for test set size when assessing and comparing test adequacy criteria. This paper makes the following contributions: (1) A review of contradictory analyses of the relationship between fault detection, test set size, and test adequacy criteria. Specifically, this paper addresses the supposed contradiction of prior work and explains why test set size is neither a confounding variable, as previously suggested, nor an independent variable that should be experimentally manipulated. (2) An explication and discussion of the experimental design and sampling strategies of prior work, together with a discussion of conceptual and statistical problems, and specific guidelines for future work. (3) A methodology for comparing test-adequacy criteria on an equal basis, which accounts for test set size by treating it as a covariate. (4) An empirical evaluation that compares the effectiveness of coverage-based and mutation-based testing to one another and random testing. Additionally, this paper proposes probabilistic coupling, a methodology for approximating the representativeness of a set of test goals for a given set of real faults.

Yiqun Chen

University of Washington

Rahul Gopinath

CISPA Helmholtz Center for Information Security

Germany

Anita Tadakamalla

George Mason University, USA

United States

Michael D. Ernst

University of Washington, USA

Reid Holmes

University of British Columbia

Canada

Gordon Fraser

University of Passau

Germany

Paul Ammann

George Mason University, USA

René Just

University of Washington, USA

WebAssembly is a new programming language built for better performance in web applications. It defines a binary code format and a text representation for the code. At first glance, WebAssembly files are not easily understandable to human readers, regardless of the experience level. As a result, distributed third-party WebAssembly modules need to be implicitly trusted by developers as verifying the functionality requires significant effort. To this end, we develop an automated classification tool WASim for identifying the purpose of WebAssembly programs by analyzing features at the module-level. It assigns purpose labels to a module in order to assist developers in understanding the binary module. The code for WASim is available at https://github.com/WASimilarity/WASim and a video demo is available at https://youtu.be/usfYFIeTy0U.

Alan Romano

University at Buffalo, SUNY

United States

Weihang Wang

University at Buffalo, SUNY

United States

In this paper, we present Sosed, a tool for discovering similar software projects. We use fastText to compute the embeddings of sub-tokens into a dense space for 120,000 GitHub repositories in 200 languages. Then, we cluster embeddings to identify groups of semantically similar sub-tokens that reflect topics in source code. We use a dataset of 9 million GitHub projects as a reference search base. To identify similar projects, we compare the distributions of clusters among their sub-tokens. The tool receives an arbitrary project as input, extracts sub-tokens in 16 most popular programming languages, computes cluster distribution, and finds projects with the closest distribution in the search base. We labeled sub-token clusters with short descriptions to enable Sosed to produce interpretable output.

Sosed is available at https://github.com/JetBrains-Research/sosed/. The tool demo is available at https://www.youtube.com/watch?v=LYLkztCGRt8. The multi-language extractor of sub-tokens is available separately at https://github.com/JetBrains-Research/identifiers-extractor/.

Egor Bogomolov

JetBrains Research

Yaroslav Golubev

JetBrains Research, ITMO University

Russia

Artyom Lobanov

JetBrains Research

Vladimir Kovalenko

JetBrains Research, JetBrains N.V.

Timofey Bryksin

JetBrains Research, Saint Petersburg State University

Russia