On Matching Log Analysis to Source Code: A Systematic Mapping Study

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00357796" target="_blank" >RIV/68407700:21230/20:00357796 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1145/3400286.3418262" target="_blank" >https://doi.org/10.1145/3400286.3418262</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3400286.3418262" target="_blank" >10.1145/3400286.3418262</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Matching Log Analysis to Source Code: A Systematic Mapping Study

Popis výsledku v původním jazyce

Logging is a vital part of the software development process. Developers use program logging to monitor program execution and identify errors and anomalies. These errors may also cause uncaught exceptions and generate stack traces that help identify the point of error. Both of these sources contain information that can be matched to points in the source code, but manual log analysis is challenging for large systems that create large volumes of logs and have large codebases. In this paper, we contribute a systematic mapping study to determine the state-of-the-art tools and methods used to perform automatic log analysis and stack trace analysis and match the extracted information back to the program's source code. We analyzed 16 publications that address this issue, summarizing their strategies and goals, and we identified open research directions from this body of work.

Název v anglickém jazyce

On Matching Log Analysis to Source Code: A Systematic Mapping Study

Popis výsledku anglicky

Logging is a vital part of the software development process. Developers use program logging to monitor program execution and identify errors and anomalies. These errors may also cause uncaught exceptions and generate stack traces that help identify the point of error. Both of these sources contain information that can be matched to points in the source code, but manual log analysis is challenging for large systems that create large volumes of logs and have large codebases. In this paper, we contribute a systematic mapping study to determine the state-of-the-art tools and methods used to perform automatic log analysis and stack trace analysis and match the extracted information back to the program's source code. We analyzed 16 publications that address this issue, summarizing their strategies and goals, and we identified open research directions from this body of work.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the International Conference on Research in Adaptive and Convergent Systems

ISBN

978-1-4503-8025-6

ISSN

2153-1633

e-ISSN

—

Počet stran výsledku

7

Strana od-do

181-187

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Gwangju

Datum konání akce

13. 10. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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