Survey on Trust in Software Engineering for Autonomous Dynamic Ecosystems
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00130119" target="_blank" >RIV/00216224:14330/23:00130119 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1145/3555776.3577702" target="_blank" >https://doi.org/10.1145/3555776.3577702</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3555776.3577702" target="_blank" >10.1145/3555776.3577702</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Survey on Trust in Software Engineering for Autonomous Dynamic Ecosystems
Popis výsledku v původním jazyce
Software systems across various application domains are undergoing a major shift, from static systems of systems to dynamic ecosystems characterized by largely autonomous software agents, engaging in mutual coalitions and partnerships to complete complex collaborative tasks. One of the key challenges facing software engineering along with this shift, is our preparedness to leverage the concept of mutual trust building among the dynamic system components, to support safe collaborations with (possibly malicious or misbehaving) components outside the boundaries of our control. To support safe evolution towards dynamic software ecosystems, this paper examines the current progress in the research on trust in software engineering across various application domains. To this end, it presents a survey of existing work in this area, and suggests the directions in which further research is needed. These directions include the research of social metrics supporting trust assessment, fine-grained quantification of trust-assessment results, and opening the discussion on governance mechanisms responsible for trust-score management and propagation across the integrated software ecosystems.
Název v anglickém jazyce
Survey on Trust in Software Engineering for Autonomous Dynamic Ecosystems
Popis výsledku anglicky
Software systems across various application domains are undergoing a major shift, from static systems of systems to dynamic ecosystems characterized by largely autonomous software agents, engaging in mutual coalitions and partnerships to complete complex collaborative tasks. One of the key challenges facing software engineering along with this shift, is our preparedness to leverage the concept of mutual trust building among the dynamic system components, to support safe collaborations with (possibly malicious or misbehaving) components outside the boundaries of our control. To support safe evolution towards dynamic software ecosystems, this paper examines the current progress in the research on trust in software engineering across various application domains. To this end, it presents a survey of existing work in this area, and suggests the directions in which further research is needed. These directions include the research of social metrics supporting trust assessment, fine-grained quantification of trust-assessment results, and opening the discussion on governance mechanisms responsible for trust-score management and propagation across the integrated software ecosystems.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
10200 - Computer and information sciences
Návaznosti výsledku
Projekt
Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2023
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
ISBN
9781450395175
ISSN
—
e-ISSN
—
Počet stran výsledku
8
Strana od-do
1490-1497
Název nakladatele
ACM
Místo vydání
Neuveden
Místo konání akce
Neuveden
Datum konání akce
1. 1. 2023
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
001124308100211