66,800 research outputs found
Fairness Testing: Testing Software for Discrimination
This paper defines software fairness and discrimination and develops a
testing-based method for measuring if and how much software discriminates,
focusing on causality in discriminatory behavior. Evidence of software
discrimination has been found in modern software systems that recommend
criminal sentences, grant access to financial products, and determine who is
allowed to participate in promotions. Our approach, Themis, generates efficient
test suites to measure discrimination. Given a schema describing valid system
inputs, Themis generates discrimination tests automatically and does not
require an oracle. We evaluate Themis on 20 software systems, 12 of which come
from prior work with explicit focus on avoiding discrimination. We find that
(1) Themis is effective at discovering software discrimination, (2)
state-of-the-art techniques for removing discrimination from algorithms fail in
many situations, at times discriminating against as much as 98% of an input
subdomain, (3) Themis optimizations are effective at producing efficient test
suites for measuring discrimination, and (4) Themis is more efficient on
systems that exhibit more discrimination. We thus demonstrate that fairness
testing is a critical aspect of the software development cycle in domains with
possible discrimination and provide initial tools for measuring software
discrimination.Comment: Sainyam Galhotra, Yuriy Brun, and Alexandra Meliou. 2017. Fairness
Testing: Testing Software for Discrimination. In Proceedings of 2017 11th
Joint Meeting of the European Software Engineering Conference and the ACM
SIGSOFT Symposium on the Foundations of Software Engineering (ESEC/FSE),
Paderborn, Germany, September 4-8, 2017 (ESEC/FSE'17).
https://doi.org/10.1145/3106237.3106277, ESEC/FSE, 201
Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction.
Tumor heterogeneity is a limiting factor in cancer treatment and in the discovery of biomarkers to personalize it. We describe a computational purification tool, ISOpure, to directly address the effects of variable normal tissue contamination in clinical tumor specimens. ISOpure uses a set of tumor expression profiles and a panel of healthy tissue expression profiles to generate a purified cancer profile for each tumor sample and an estimate of the proportion of RNA originating from cancerous cells. Applying ISOpure before identifying gene signatures leads to significant improvements in the prediction of prognosis and other clinical variables in lung and prostate cancer
SKIRT: the design of a suite of input models for Monte Carlo radiative transfer simulations
The Monte Carlo method is the most popular technique to perform radiative
transfer simulations in a general 3D geometry. The algorithms behind and
acceleration techniques for Monte Carlo radiative transfer are discussed
extensively in the literature, and many different Monte Carlo codes are
publicly available. On the contrary, the design of a suite of components that
can be used for the distribution of sources and sinks in radiative transfer
codes has received very little attention. The availability of such models, with
different degrees of complexity, has many benefits. For example, they can serve
as toy models to test new physical ingredients, or as parameterised models for
inverse radiative transfer fitting. For 3D Monte Carlo codes, this requires
algorithms to efficiently generate random positions from 3D density
distributions. We describe the design of a flexible suite of components for the
Monte Carlo radiative transfer code SKIRT. The design is based on a combination
of basic building blocks (which can be either analytical toy models or
numerical models defined on grids or a set of particles) and the extensive use
of decorators that combine and alter these building blocks to more complex
structures. For a number of decorators, e.g. those that add spiral structure or
clumpiness, we provide a detailed description of the algorithms that can be
used to generate random positions. Advantages of this decorator-based design
include code transparency, the avoidance of code duplication, and an increase
in code maintainability. Moreover, since decorators can be chained without
problems, very complex models can easily be constructed out of simple building
blocks. Finally, based on a number of test simulations, we demonstrate that our
design using customised random position generators is superior to a simpler
design based on a generic black-box random position generator.Comment: 15 pages, 4 figures, accepted for publication in Astronomy and
Computin
A survey on test suite reduction frameworks and tools
Software testing is a widely accepted practice that ensures the quality of a System under Test (SUT). However, the gradual increase of the test suite size demands high portion of testing budget and time. Test Suite Reduction (TSR) is considered a potential approach to deal with the test suite size problem. Moreover, a complete automation support is highly recommended for software testing to adequately meet the challenges of a resource constrained testing environment. Several TSR frameworks and tools have been proposed to efficiently address the test-suite size problem. The main objective of the paper is to comprehensively review the state-of-the-art TSR frameworks to highlights their strengths and weaknesses. Furthermore, the paper focuses on devising a detailed thematic taxonomy to classify existing literature that helps in understanding the underlying issues and proof of concept. Moreover, the paper investigates critical aspects and related features of TSR frameworks and tools based on a set of defined parameters. We also rigorously elaborated various testing domains and approaches followed by the extant TSR frameworks. The results reveal that majority of TSR frameworks focused on randomized unit testing, and a considerable number of frameworks lacks in supporting multi-objective optimization problems. Moreover, there is no generalized framework, effective for testing applications developed in any programming domain. Conversely, Integer Linear Programming (ILP) based TSR frameworks provide an optimal solution for multi-objective optimization problems and improve execution time by running multiple ILP in parallel. The study concludes with new insights and provides an unbiased view of the state-of-the-art TSR frameworks. Finally, we present potential research issues for further investigation to anticipate efficient TSR frameworks
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