43,258 research outputs found
Time-Space Efficient Regression Testing for Configurable Systems
Configurable systems are those that can be adapted from a set of options.
They are prevalent and testing them is important and challenging. Existing
approaches for testing configurable systems are either unsound (i.e., they can
miss fault-revealing configurations) or do not scale. This paper proposes
EvoSPLat, a regression testing technique for configurable systems. EvoSPLat
builds on our previously-developed technique, SPLat, which explores all
dynamically reachable configurations from a test. EvoSPLat is tuned for two
scenarios of use in regression testing: Regression Configuration Selection
(RCS) and Regression Test Selection (RTS). EvoSPLat for RCS prunes
configurations (not tests) that are not impacted by changes whereas EvoSPLat
for RTS prunes tests (not configurations) which are not impacted by changes.
Handling both scenarios in the context of evolution is important. Experimental
results show that EvoSPLat is promising. We observed a substantial reduction in
time (22%) and in the number of configurations (45%) for configurable Java
programs. In a case study on a large real-world configurable system (GCC),
EvoSPLat reduced 35% of the running time. Comparing EvoSPLat with sampling
techniques, 2-wise was the most efficient technique, but it missed two bugs
whereas EvoSPLat detected all bugs four times faster than 6-wise, on average.Comment: 14 page
Visualizing test diversity to support test optimisation
Diversity has been used as an effective criteria to optimise test suites for
cost-effective testing. Particularly, diversity-based (alternatively referred
to as similarity-based) techniques have the benefit of being generic and
applicable across different Systems Under Test (SUT), and have been used to
automatically select or prioritise large sets of test cases. However, it is a
challenge to feedback diversity information to developers and testers since
results are typically many-dimensional. Furthermore, the generality of
diversity-based approaches makes it harder to choose when and where to apply
them. In this paper we address these challenges by investigating: i) what are
the trade-off in using different sources of diversity (e.g., diversity of test
requirements or test scripts) to optimise large test suites, and ii) how
visualisation of test diversity data can assist testers for test optimisation
and improvement. We perform a case study on three industrial projects and
present quantitative results on the fault detection capabilities and redundancy
levels of different sets of test cases. Our key result is that test similarity
maps, based on pair-wise diversity calculations, helped industrial
practitioners identify issues with their test repositories and decide on
actions to improve. We conclude that the visualisation of diversity information
can assist testers in their maintenance and optimisation activities
A Survey on Software Testing Techniques using Genetic Algorithm
The overall aim of the software industry is to ensure delivery of high
quality software to the end user. To ensure high quality software, it is
required to test software. Testing ensures that software meets user
specifications and requirements. However, the field of software testing has a
number of underlying issues like effective generation of test cases,
prioritisation of test cases etc which need to be tackled. These issues demand
on effort, time and cost of the testing. Different techniques and methodologies
have been proposed for taking care of these issues. Use of evolutionary
algorithms for automatic test generation has been an area of interest for many
researchers. Genetic Algorithm (GA) is one such form of evolutionary
algorithms. In this research paper, we present a survey of GA approach for
addressing the various issues encountered during software testing.Comment: 13 Page
Is the Stack Distance Between Test Case and Method Correlated With Test Effectiveness?
Mutation testing is a means to assess the effectiveness of a test suite and
its outcome is considered more meaningful than code coverage metrics. However,
despite several optimizations, mutation testing requires a significant
computational effort and has not been widely adopted in industry. Therefore, we
study in this paper whether test effectiveness can be approximated using a more
light-weight approach. We hypothesize that a test case is more likely to detect
faults in methods that are close to the test case on the call stack than in
methods that the test case accesses indirectly through many other methods.
Based on this hypothesis, we propose the minimal stack distance between test
case and method as a new test measure, which expresses how close any test case
comes to a given method, and study its correlation with test effectiveness. We
conducted an empirical study with 21 open-source projects, which comprise in
total 1.8 million LOC, and show that a correlation exists between stack
distance and test effectiveness. The correlation reaches a strength up to 0.58.
We further show that a classifier using the minimal stack distance along with
additional easily computable measures can predict the mutation testing result
of a method with 92.9% precision and 93.4% recall. Hence, such a classifier can
be taken into consideration as a light-weight alternative to mutation testing
or as a preceding, less costly step to that.Comment: EASE 201
A Model to Estimate First-Order Mutation Coverage from Higher-Order Mutation Coverage
The test suite is essential for fault detection during software development.
First-order mutation coverage is an accurate metric to quantify the quality of
the test suite. However, it is computationally expensive. Hence, the adoption
of this metric is limited. In this study, we address this issue by proposing a
realistic model able to estimate first-order mutation coverage using only
higher-order mutation coverage. Our study shows how the estimation evolves
along with the order of mutation. We validate the model with an empirical study
based on 17 open-source projects.Comment: 2016 IEEE International Conference on Software Quality, Reliability,
and Security. 9 page
The Co-Evolution of Test Maintenance and Code Maintenance through the lens of Fine-Grained Semantic Changes
Automatic testing is a widely adopted technique for improving software
quality. Software developers add, remove and update test methods and test
classes as part of the software development process as well as during the
evolution phase, following the initial release. In this work we conduct a large
scale study of 61 popular open source projects and report the relationships we
have established between test maintenance, production code maintenance, and
semantic changes (e.g, statement added, method removed, etc.). performed in
developers' commits.
We build predictive models, and show that the number of tests in a software
project can be well predicted by employing code maintenance profiles (i.e., how
many commits were performed in each of the maintenance activities: corrective,
perfective, adaptive). Our findings also reveal that more often than not,
developers perform code fixes without performing complementary test maintenance
in the same commit (e.g., update an existing test or add a new one). When
developers do perform test maintenance, it is likely to be affected by the
semantic changes they perform as part of their commit.
Our work is based on studying 61 popular open source projects, comprised of
over 240,000 commits consisting of over 16,000,000 semantic change type
instances, performed by over 4,000 software engineers.Comment: postprint, ICSME 201
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