Validation of Scientific Programs

Howden, William E.

doi:10.1145/356876.356880

Cited by 25 publications

(5 citation statements)

References 23 publications

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“…Post and Votta [2005], for example refers to the need for a paradigm shift in this area. Indeed, advocates of software validation in scientific programming were making their arguments as early as the 1960s [Howden 1982;Naylor and Finger 1967]. There are several reasons for a lack of software quality assurance practice in scientific programming reported in the literature: -Theoretical (often mathematical) models and their associated software implementations and empirical data become conflated [Easterbrook and Johns 2009;Kelly 2015;Killcoyne and Boyle 2009;Spinellis and Spencer 2011;Voinov and Shugart 2013].…”

Section: Quality Assurance Practicesmentioning

confidence: 99%

Bridging the Chasm

Storer

2017

ACM Comput. Surv.

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The use of software is pervasive in all fields of science. Associated software development efforts may be very large, long lived, and complex, requiring the commitment of significant resources. However, several authors have argued that the “gap” or “chasm” between software engineering and scientific programming is a serious risk to the production of reliable scientific results, as demonstrated in a number of case studies. This article reviews the research that addresses the gap, exploring how both software engineering and research practice may need to evolve to accommodate the use of software in science.

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Section: Quality Assurance Practicesmentioning

confidence: 99%

Bridging the Chasm

Storer

2017

ACM Comput. Surv.

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“…No. of Program Path condition over R Normalized FPSE path conditions constraints 6 foo1.c X > 0, X + 10 12 = 10 12 X > 0.0, T 1 = X ⊕ 1.0e12, T 1 = 1.0e12 3 7 foo2.c X < 10 4 , X + 10 12 > 10 12 X < 10000.0, T 1 = X ⊕ 1.0e12, Secondly, path feasibility experiments were performed with FPSE on path conditions extracted from programs foo1.c and foo2.c given in the introductory part of the paper (Figures 1 and 2), from the program howden.c, which is a small-sized numeric computation extracted from the work of Howden [40] and from the program power.c (Figure 9). For these programs, path conditions are given in the bottom part of Table III.…”

Section: Programsmentioning

confidence: 99%

Symbolic execution of floating‐point computations

Botella

Gotlieb

Michel

2005

Software Testing Verif & Rel

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International audienceSymbolic execution is a classical program testing technique which evaluates a selected control flow path with symbolic input data. A constraint solver can be used to enforce the satisfiability of the extracted path conditions as well as to derive test data. Whenever path conditions contain floating-point computations, a common strategy consists of using a constraint solver over the rationals or the reals. Unfortunately, even in a fully IEEE-754 compliant environment, this leads not only to approximations but also can compromise correctness: a path can be labelled as infeasible although there exists floating-point input data that satisfy it. In this paper, we address the peculiarities of the symbolic execution of program with floating-point numbers. Issues in the symbolic execution of this kind of programs are carefully examined and a constraint solver is described that supports constraints over floating-point numbers. Preliminary experimental results demonstrate the value of our approach

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“…Testing scientific computational software has been the subject of research for many years (see, for example, [3,4,6,7]). Because of the increase in size and complexity of such software, automation of scientific software testing is very important.…”

Section: Introductionmentioning

confidence: 99%