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DTSTART:20211031T030000
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RDATE:20221030T030000
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UID:calendar.24850.field_data.0@www.open.diag.uniroma1.it
DTSTAMP:20260404T175649Z
CREATED:20220408T141914Z
DESCRIPTION:AbstractSoftware testing is an effective practice used to check
  whether the software products that are being developed match their expect
 ed requirements. Modern industrial systems spend a large amount of resourc
 es on software testing by running thousands or even millions of tests ever
 y day. Many techniques have been presented in the academic literature to t
 ame the costs of software testing. For example\, they reorder the test sui
 te or select a subset of it to find software failures quicker. However\, m
 ost existing techniques are too expensive for handling modern massive syst
 ems and depend on artifacts\, such as code coverage metrics\, that are not
  commonly available at a large scale. In this talk\, we show how informati
 on retrieval and data mining techniques can help while applied to software
  testing problems. In particular\, we present two approaches that have com
 parable effectiveness to state-of-the-art techniques\, while providing hug
 e gains in terms of efficiency\, as shown by experimental results. Bio Ske
 tchEmilio Cruciani got his Ph.D. in Computer Science in 2019 at the Gran S
 asso Science Institute. He is currently a postdoctoral researcher in the B
 ig Data Algorithms Group of the Paris-Lodron University of Salzburg. Previ
 ously he was a postdoctoral researcher in the Efficient Algorithms Group (
 University of Salzburg) and the COATI team in INRIA Sophia Antipolis. His 
 research interests include the analysis of dynamics on networks and the de
 sign and implementation of scalable algorithms and heuristics for large da
 tasets. His line of research on scalable approaches for software testing i
 s funded by two Facebook Research Awards (2019\, 2021). Il will be possibl
 e to attend remotely at the following Zoom link: https://uniroma1.zoom.us/
 j/87006718821?pwd=SG9kemlydHY1UzEvT1JoUnYyQUdiUT09 Login to Zoom with unir
 oma1.it credentials is required before connecting to the link above
DTSTART;TZID=Europe/Paris:20220419T150000
DTEND;TZID=Europe/Paris:20220419T150000
LAST-MODIFIED:20220415T105950Z
LOCATION:Aula Magna DIAG via Ariosto 25 I piano
SUMMARY:Software Testing Meets Big Data: Scalable Approaches for Large Test
  Suites - Emilio Cruciani - Paris-Lodron University of Salzburg
URL;TYPE=URI:http://www.open.diag.uniroma1.it/node/24850
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