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Matei Schiopu

dblp:375/6071 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0009-6507-9278ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Software testing · 77% Program verification · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Software testing › system testing
robotic system testing
0.812024
Runtime Verification and Field-Based Testing for ROS-Based Robotic Systems · IEEE Trans. Software Eng. 2024
Program verification › dynamic verification
runtime verification
0.212024
Runtime Verification and Field-Based Testing for ROS-Based Robotic Systems · IEEE Trans. Software Eng. 2024

Methods — techniques the papers use, named apart from their topics

repository mining · 0.8questionnaire · 0.8literature review · 0.8
YearPublicationVenuePosition
2024 Runtime Verification and Field-Based Testing for ROS-Based Robotic Systems
abstract
Robotic systems are becoming pervasive and adopted in increasingly many domains, such as manufacturing, healthcare, and space exploration. To this end, engineering software has emerged as a crucial discipline for building maintainable and reusable robotic systems. The field of robotics software engineering research has received increasing attention, fostering autonomy as a fundamental goal. However, robotics developers are still challenged trying to achieve this goal given that simulation is not able to deliver solutions to realistically emulate real-world phenomena. Robots also need to operate in unpredictable and uncontrollable environments, which require safe and trustworthy self-adaptation capabilities implemented in software. Typical techniques to address the challenges are runtime verification, field-based testing, and mitigation techniques that enable fail-safe solutions. However, there is no clear guidance to architect ROS-based systems to enable and facilitate runtime verification and field-based testing. This paper aims to fill in this gap by providing guidelines that can help developers and quality assurance (QA) teams when developing, verifying or testing their robots in the field. These guidelines are carefully tailored to address the challenges and requirements of testing robotics systems in real-world scenarios. We conducted (i) a literature review on studies addressing runtime verification and field-based testing for robotic systems, (ii) mined ROS-based applications repositories, and (iii) validated the applicability, clarity, and usefulness via two questionnaires with 55 answers overall. We contribute 20 guidelines: 8 for developers and 12 for QA teams formulated for researchers and practitioners in robotic software engineering. Finally, we map our guidelines to open challenges thus far in runtime verification and field-based testing for ROS-based systems and, we outline promising research directions in the field.Guidelines website and replication package:https://ros-rvft.github.io.
Ricardo Caldas, Juan Antonio Piñera García, Matei Schiopu, Patrizio Pelliccione, Genaína Nunes Rodrigues, Thorsten Berger
IEEE Trans. Software Eng.3