Code coverage

In software engineering, code coverage is a percentage measure of the degree to which the source code of a program is executed when a particular test suite is run. A program with high test coverage has more of its source code executed during testing, which suggests it has a lower chance of containing undetected software bugs compared to a program with low test coverage. Many different metrics can be used to calculate test coverage. Some of the most basic are the percentage of program subroutines and the percentage of program statements called during execution of the test suite. Test coverage was among the first methods invented for systematic software testing. The first published reference was by Miller and Maloney in Communications of the ACM, in 1963. Coverage criteria To measure what percentage of code has been executed by a test suite, one or more coverage criteria are used. These are usually defined as rules or requirements, which a test suite must satisfy. Bas

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (24)

Related publications

Related people (2)

Related people

Related units (3)

Related units

Related concepts (7)

Related concepts

Related courses (10)

Related courses

Related lectures (23)

Related lectures

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Kamil Bennani-Smires, Martin Jaggi, Claudiu-Cristian Musat

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as human-facing use cases. Existing supervised systems for keyphrase extraction require large amounts of labeled training data and generalize very poorly outside the domain of the training data. At the same time, unsupervised systems found in the literature have poor accuracy, and often do not generalize well, as they require the input document to belong to a larger corpus also given as input. Furthermore, both supervised and unsupervised methods are often too slow for real-time scenarios and suffer from over-generation. Addressing these drawbacks, in this paper, we introduce an unsupervised method for keyphrase extraction from single documents that leverages sentence embeddings. By selecting phrases whose semantic embeddings are close to the embeddings of the whole document, we are able to separate the best candidate phrases from the rest. We show that our embedding-based method is not only simpler, but also more effective than graph-based state of the art systems, achieving higher F-scores on standard datasets. Simplicity is a significant advantage, especially when processing large amounts of documents from the Web, resulting in considerable speed gains. Moreover, we describe how to increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study including over 200 votes showed that, although reducing the phrase semantic overlap leads to no gains in terms of F-score, our diversity enriched selection is preferred by humans.

2018

Fair colorful k-center clustering

Xinrui Jia, Ola Nils Anders Svensson

An instance of colorful k-center consists of points in a metric space that are colored red or blue, along with an integer k and a coverage requirement for each color. The goal is to find the smallest radius rho such that there exist balls of radius rho around k of the points that meet the coverage requirements. The motivation behind this problem is twofold. First, from fairness considerations: each color/group should receive a similar service guarantee, and second, from the algorithmic challenges it poses: this problem combines the difficulties of clustering along with the subset-sum problem. In particular, we show that this combination results in strong integrality gap lower bounds for several natural linear programming relaxations. Our main result is an efficient approximation algorithm that overcomes these difficulties to achieve an approximation guarantee of 3, nearly matching the tight approximation guarantee of 2 for the classical k-center problem which this problem generalizes. algorithms either opened more than k centers or only worked in the special case when the input points are in the plane.

SPRINGER HEIDELBERG2021

FuzzGen: Automatic Fuzzer Generation

Mathias Josef Payer

Fuzzing is a testing technique to discover unknown vulnerabilities in software. When applying fuzzing to libraries, the core idea of supplying random input remains unchanged, yet it is non-trivial to achieve good code coverage. Libraries cannot run as standalone programs, but instead are invoked through another application. Triggering code deep in a library remains challenging as specific sequences of API calls are required to build up the necessary state. Libraries are diverse and have unique interfaces that require unique fuzzers, so far written by a human analyst. To address this issue, we present FuzzGen, a tool for automatically synthesizing fuzzers for complex libraries in a given environment. FuzzGen leverages a whole system analysis to infer the library's interface and synthesizes fuzzers specifically for that library. FuzzGen requires no human interaction and can be applied to a wide range of libraries. Furthermore, the generated fuzzers leverage LibFuzzer to achieve better code coverage and expose bugs that reside deep in the library. FuzzGen was evaluated on Debian and the Android Open Source Project (AOSP) selecting 7 libraries to generate fuzzers. So far, we have found 17 previously unpatched vulnerabilities with 6 assigned CVEs. The generated fuzzers achieve an average of 54.94% code coverage; an improvement of 6.94% when compared to manually written fuzzers, demonstrating the effectiveness and generality of FuzzGen.

USENIX ASSOC2020

Software testing

Software testing is the act of examining the artifacts and the behavior of the software under test by validation and verification. Software testing can also provide an objective, independent view of t

Cyclomatic complexity

Cyclomatic complexity is a software metric used to indicate the complexity of a program. It is a quantitative measure of the number of linearly independent paths through a program's source code. It wa

Fault injection

In computer science, fault injection is a testing technique for understanding how computing systems behave when stressed in unusual ways. This can be achieved using physical- or software-based means

CS-305: Software engineering

This course teaches the basics of modern software development: designing software, working in a team, writing good code, shipping software, and evolving software. It emphasizes building software that meets high standards of quality, reliability, security, and manageability.

COM-301: Computer security

This is an introductory course to computer security and privacy. Its goal is to provide students with means to reason about security and privacy problems, and provide them with tools to confront them.

CS-412: Software security

This course focuses on software security fundamentals, secure coding guidelines and principles, and advanced software security concepts. Students learn to assess and understand threats, learn how to design and implement secure software systems, and get hands-on experience with security pitfalls.