BMS software reliability prediction as a function of residual defect density using SIL dynamic metrics
Narayanappa, Manjunatha (2022-03-19)
BMS software reliability prediction as a function of residual defect density using SIL dynamic metrics
(19.03.2022)
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Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2022051636012
https://urn.fi/URN:NBN:fi-fe2022051636012
Tiivistelmä
The continuously increasing complexity and computerization of automotive systems
correlate directly to increased expectations from customers on the reliability of the
system. Especially for battery management systems, the malfunction, defect, or loss
of functionality can potentially lead to hazards that might compromise the safety of
passengers and the environment. Software development being the major part of em-
bedded product development, quality metrics are important in deciding the overall
reliability of the system. However, unlike hardware, the software reliability is diffi-
cult to estimate since reliability modeling requires a failure dataset that is collected
for a substantial amount of time for accurate prediction which is not feasible during
the early development phases of software.
In this work, we propose the reliability prediction of the embedded software based
on the residual defect density as a failure rate function. To estimate residual defect
density, it is important to model the prediction of residual defects. The traditional
approaches generally use static metrics such as code metrics from a single software
release along with failure data to predict and forecast software defects. We propose
a new approach by introducing dynamic metrics in residual defect prediction along
with code and process metrics. Dynamic metrics are collected using the SIL tool
which verifies the functional compliance of the software on vehicle test data. The
impact of dynamic metrics on residual defect prediction and reliability prediction is
evaluated by comparing the performance of regression-based machine learning algo-
rithms such as Normal Regression, Decision Tree, Random Forest, Artificial Neural
Network, and K-Nearest Neighbour. The faults are injected to verify the usefulness
of the SIL tool in capturing the dynamic metrics and their impact on the reliability
of the software.
The results from the experiment are positive, indicating the improvement in the
residual defects prediction with dynamic metrics. Although, some algorithms (Ran-
dom Forest and Decision Tree) performed better than the others (Linear Regression
and K-Nearest Neighbour). In terms of reliability estimation, the dynamics metrics
are able to capture the faults introduced and slightly improved the reliability of the
software compared to actual software release versions
correlate directly to increased expectations from customers on the reliability of the
system. Especially for battery management systems, the malfunction, defect, or loss
of functionality can potentially lead to hazards that might compromise the safety of
passengers and the environment. Software development being the major part of em-
bedded product development, quality metrics are important in deciding the overall
reliability of the system. However, unlike hardware, the software reliability is diffi-
cult to estimate since reliability modeling requires a failure dataset that is collected
for a substantial amount of time for accurate prediction which is not feasible during
the early development phases of software.
In this work, we propose the reliability prediction of the embedded software based
on the residual defect density as a failure rate function. To estimate residual defect
density, it is important to model the prediction of residual defects. The traditional
approaches generally use static metrics such as code metrics from a single software
release along with failure data to predict and forecast software defects. We propose
a new approach by introducing dynamic metrics in residual defect prediction along
with code and process metrics. Dynamic metrics are collected using the SIL tool
which verifies the functional compliance of the software on vehicle test data. The
impact of dynamic metrics on residual defect prediction and reliability prediction is
evaluated by comparing the performance of regression-based machine learning algo-
rithms such as Normal Regression, Decision Tree, Random Forest, Artificial Neural
Network, and K-Nearest Neighbour. The faults are injected to verify the usefulness
of the SIL tool in capturing the dynamic metrics and their impact on the reliability
of the software.
The results from the experiment are positive, indicating the improvement in the
residual defects prediction with dynamic metrics. Although, some algorithms (Ran-
dom Forest and Decision Tree) performed better than the others (Linear Regression
and K-Nearest Neighbour). In terms of reliability estimation, the dynamics metrics
are able to capture the faults introduced and slightly improved the reliability of the
software compared to actual software release versions