What we eval in the shadows: A large-scale study of eval in R programs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F21%3A00354132" target="_blank" >RIV/68407700:21240/21:00354132 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3485502" target="_blank" >https://doi.org/10.1145/3485502</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3485502" target="_blank" >10.1145/3485502</a>

Jazyk výsledku

angličtina

Název v původním jazyce

What we eval in the shadows: A large-scale study of eval in R programs

Popis výsledku v původním jazyce

Most dynamic languages allow users to turn text into code using various functions, often named eval, with language-dependent semantics. The widespread use of these reflective functions hinders static analysis and prevents compilers from performing optimizations. This paper aims to provide a better sense of why programmers use eval. Understanding why eval is used in practice is key to finding ways to mitigate its negative impact. We have reasons to believe that reflective feature usage is language and application domain-specific; we focus on data science code written in R and compare our results to previous work that analyzed web programming in JavaScript. We analyze 49,296,059 calls to eval from 240,327 scripts extracted from 15,401 R packages. We find that eval is indeed in widespread use; R's eval is more pervasive and arguably dangerous than what was previously reported for JavaScript.

Název v anglickém jazyce

What we eval in the shadows: A large-scale study of eval in R programs

Popis výsledku anglicky

Most dynamic languages allow users to turn text into code using various functions, often named eval, with language-dependent semantics. The widespread use of these reflective functions hinders static analysis and prevents compilers from performing optimizations. This paper aims to provide a better sense of why programmers use eval. Understanding why eval is used in practice is key to finding ways to mitigate its negative impact. We have reasons to believe that reflective feature usage is language and application domain-specific; we focus on data science code written in R and compare our results to previous work that analyzed web programming in JavaScript. We analyze 49,296,059 calls to eval from 240,327 scripts extracted from 15,401 R packages. We find that eval is indeed in widespread use; R's eval is more pervasive and arguably dangerous than what was previously reported for JavaScript.

Druh

J_ost - Ostatní články v recenzovaných periodicích

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF15_003%2F0000421" target="_blank" >EF15_003/0000421: Big Code: Škálovatelná analýza rozsáhlých bází programů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Proceedings of the ACM on Programming Languages (PACMPL)

ISSN

2475-1421

e-ISSN

—

Svazek periodika

5

Číslo periodika v rámci svazku

OOPSLA

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

—

Kód UT WoS článku

000731569200029

EID výsledku v databázi Scopus

2-s2.0-85117586856