81,930 research outputs found
Do System Test Cases Grow Old?
Companies increasingly use either manual or automated system testing to
ensure the quality of their software products. As a system evolves and is
extended with new features the test suite also typically grows as new test
cases are added. To ensure software quality throughout this process the test
suite is continously executed, often on a daily basis. It seems likely that
newly added tests would be more likely to fail than older tests but this has
not been investigated in any detail on large-scale, industrial software
systems. Also it is not clear which methods should be used to conduct such an
analysis. This paper proposes three main concepts that can be used to
investigate aging effects in the use and failure behavior of system test cases:
test case activation curves, test case hazard curves, and test case half-life.
To evaluate these concepts and the type of analysis they enable we apply them
on an industrial software system containing more than one million lines of
code. The data sets comes from a total of 1,620 system test cases executed a
total of more than half a million times over a time period of two and a half
years. For the investigated system we find that system test cases stay active
as they age but really do grow old; they go through an infant mortality phase
with higher failure rates which then decline over time. The test case half-life
is between 5 to 12 months for the two studied data sets.Comment: Updated with nicer figs without border around the
A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency
In this paper, we address the problem of asset performance monitoring, with the intention
of both detecting any potential reliability problem and predicting any loss of energy consumption
e ciency. This is an important concern for many industries and utilities with very intensive
capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an
approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically
with Association Rule (AR) Mining. The combination of these two techniques can now be done
using software which can handle large volumes of data (big data), but the process still needs to
ensure that the required amount of data will be available during the assets’ life cycle and that its
quality is acceptable. The combination of these two techniques in the proposed sequence di ers
from previous works found in the literature, giving researchers new options to face the problem.
Practical implementation of the proposed approach may lead to novel predictive maintenance models
(emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of
performance and help manage assets’ O&M accordingly. The approach is illustrated using specific
examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-
Durability Analysis of Concrete Bridge Deck Exposed to the Chloride Ions Using Direct Optimized Probabilistic Calculation
Durability of reinforced concrete structures is a
deeply discussed problem recently. Concrete structures in
the external environment are very often affected by
chloride ions from de-icing salt or sea water. Chloride ions
penetrate through the concrete cover layer
of the reinforcement and can cause eventually the
corrosion of the steel. However, when estimating the
durability of the structure, it is not sometimes possible to
express the parameters by constant values; therefore, the
probabilistic methods come in handy. Then, the variability
of inputs and outputs can be expressed by histograms. Two
probabilistic approaches were applied in this task – Monte
Carlo simulation with Simulation-Based Reliability
Assessment method, which is widely used for such type of
problems, and the Direct Optimized Probabilistic
Calculation, which is still relatively new type of approach.
The result is a comparison of mentioned methods in terms
of accuracy on the model of one-dimensional chloride
penetration with time independent diffusion coefficient by
using the Fick’s Second Law of Diffusion
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