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Implementation relations for testing through asynchronous channels
This paper concerns testing from an input output transition system (IOTS) model of a system under test that interacts with its environment through asynchronous first in first out (FIFO) channels. It explores methods for analysing an IOTS without modelling the channels. If IOTS M produces sequence then, since communications are asynchronous, output can be delayed and so a different sequence might be observed. Thus M defines a language Tr(M) of sequences that can be observed when interacting with M through FIFO channels. We define implementation relations and equivalences in terms of Tr(M): an implementation relation says how IOTS N must relate to IOTS M in order for N to be a correct implementation of M. It is important to use an appropriate implementation relation since otherwise the verdict from a test run might be incorrect and because it influences test generation. It is undecidable whether IOTS N conforms to IOTS M and so also whether there is a test case that can distinguish between two IOTSs. We also investigate the situation in which we have a finite automaton P and either wish to know whether is empty or whether Tr(M) \cap \tr(P) is empty and prove that these are undecidable. In addition, we give conditions under which conformance and intersection are decidable.This work was partially supported by EPSRC grant EP/G04354X/1:The Birth, Life and Death of Semantic Mutants
The complexity of asynchronous model based testing
This is the post-print version of the final paper published in Theoretical Computer Science. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.In model based testing (MBT), testing is based on a model MM that typically is expressed using a state-based language such as an input output transition system (IOTS). Most approaches to MBT assume that communications between the system under test (SUT) and its environment are synchronous. However, many systems interact with their environment through asynchronous channels and the presence of such channels changes the nature of testing. In this paper we investigate the situation in which the SUT interacts with its environment through asynchronous channels and the problems of producing test cases to reach a state, execute a transition, or to distinguish two states. In addition, we investigate the Oracle Problem. All four problems are explored for both FIFO and non-FIFO channels. It is known that the Oracle Problem can be solved in polynomial time for FIFO channels but we also show that the three test case generation problems can also be solved in polynomial time in the case where the IOTS is observable but the general test generation problems are EXPTIME-hard. For non-FIFO channels we prove that all of the test case generation problems are EXPTIME-hard and the Oracle Problem in NP-hard, even if we restrict attention to deterministic IOTSs
Feasibility studies in relation to the IMO Ballast Water Convention
This project is aimed to develop possibilities to overcome the difficulties which arise from the implementation of the Ballast Water Convention (IMO 2004). For this purpose, three feasibility studies have been conducted: assessment of the applicability of small scale test systems; development of protocols for testing active substance residues; risk assessment of ballast water discharge
Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
For a self-driving car to operate reliably, its perceptual system must
generalize to the end-user's environment -- ideally without additional
annotation efforts. One potential solution is to leverage unlabeled data (e.g.,
unlabeled LiDAR point clouds) collected from the end-users' environments (i.e.
target domain) to adapt the system to the difference between training and
testing environments. While extensive research has been done on such an
unsupervised domain adaptation problem, one fundamental problem lingers: there
is no reliable signal in the target domain to supervise the adaptation process.
To overcome this issue we observe that it is easy to collect unsupervised data
from multiple traversals of repeated routes. While different from conventional
unsupervised domain adaptation, this assumption is extremely realistic since
many drivers share the same roads. We show that this simple additional
assumption is sufficient to obtain a potent signal that allows us to perform
iterative self-training of 3D object detectors on the target domain.
Concretely, we generate pseudo-labels with the out-of-domain detector but
reduce false positives by removing detections of supposedly mobile objects that
are persistent across traversals. Further, we reduce false negatives by
encouraging predictions in regions that are not persistent. We experiment with
our approach on two large-scale driving datasets and show remarkable
improvement in 3D object detection of cars, pedestrians, and cyclists, bringing
us a step closer to generalizable autonomous driving.Comment: Accepted by NeurIPS 2022. Code is available at
https://github.com/YurongYou/Rote-D
Using schedulers to test probabilistic distributed systems
This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s00165-012-0244-5. Copyright Ā© 2012, British Computer Society.Formal methods are one of the most important approaches to increasing the confidence in the correctness of software systems. A formal specification can be used as an oracle in testing since one can determine whether an observed behaviour is allowed by the specification. This is an important feature of formal testing: behaviours of the system observed in testing are compared with the specification and ideally this comparison is automated. In this paper we study a formal testing framework to deal with systems that interact with their environment at physically distributed interfaces, called ports, and where choices between different possibilities are probabilistically quantified. Building on previous work, we introduce two families of schedulers to resolve nondeterministic choices among different actions of the system. The first type of schedulers, which we call global schedulers, resolves nondeterministic choices by representing the environment as a single global scheduler. The second type, which we call localised schedulers, models the environment as a set of schedulers with there being one scheduler for each port. We formally define the application of schedulers to systems and provide and study different implementation relations in this setting
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning
Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of oneās current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical accountāthe Search for Invariance (SI) hypothesisāwhich suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and interventionānot to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy
Paving the Roadway for Safety of Automated Vehicles: An Empirical Study on Testing Challenges
The technology in the area of automated vehicles is gaining speed and
promises many advantages. However, with the recent introduction of
conditionally automated driving, we have also seen accidents. Test protocols
for both, conditionally automated (e.g., on highways) and automated vehicles do
not exist yet and leave researchers and practitioners with different
challenges. For instance, current test procedures do not suffice for fully
automated vehicles, which are supposed to be completely in charge for the
driving task and have no driver as a back up. This paper presents current
challenges of testing the functionality and safety of automated vehicles
derived from conducting focus groups and interviews with 26 participants from
five countries having a background related to testing automotive safety-related
topics.We provide an overview of the state-of-practice of testing active safety
features as well as challenges that needs to be addressed in the future to
ensure safety for automated vehicles. The major challenges identified through
the interviews and focus groups, enriched by literature on this topic are
related to 1) virtual testing and simulation, 2) safety, reliability, and
quality, 3) sensors and sensor models, 4) required scenario complexity and
amount of test cases, and 5) handover of responsibility between the driver and
the vehicle.Comment: 8 page
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