24,228 research outputs found
Supporting Defect Causal Analysis in Practice with Cross-Company Data on Causes of Requirements Engineering Problems
[Context] Defect Causal Analysis (DCA) represents an efficient practice to
improve software processes. While knowledge on cause-effect relations is
helpful to support DCA, collecting cause-effect data may require significant
effort and time. [Goal] We propose and evaluate a new DCA approach that uses
cross-company data to support the practical application of DCA. [Method] We
collected cross-company data on causes of requirements engineering problems
from 74 Brazilian organizations and built a Bayesian network. Our DCA approach
uses the diagnostic inference of the Bayesian network to support DCA sessions.
We evaluated our approach by applying a model for technology transfer to
industry and conducted three consecutive evaluations: (i) in academia, (ii)
with industry representatives of the Fraunhofer Project Center at UFBA, and
(iii) in an industrial case study at the Brazilian National Development Bank
(BNDES). [Results] We received positive feedback in all three evaluations and
the cross-company data was considered helpful for determining main causes.
[Conclusions] Our results strengthen our confidence in that supporting DCA with
cross-company data is promising and should be further investigated.Comment: 10 pages, 8 figures, accepted for the 39th International Conference
on Software Engineering (ICSE'17
Software Metrics in Boa Large-Scale Software Mining Infrastructure: Challenges and Solutions
In this paper, we describe our experience implementing some of classic
software engineering metrics using Boa - a large-scale software repository
mining platform - and its dedicated language. We also aim to take an advantage
of the Boa infrastructure to propose new software metrics and to characterize
open source projects by software metrics to provide reference values of
software metrics based on large number of open source projects. Presented
software metrics, well known and proposed in this paper, can be used to build
large-scale software defect prediction models. Additionally, we present the
obstacles we met while developing metrics, and our analysis can be used to
improve Boa in its future releases. The implemented metrics can also be used as
a foundation for more complex explorations of open source projects and serve as
a guide how to implement software metrics using Boa as the source code of the
metrics is freely available to support reproducible research.Comment: Chapter 8 of the book "Software Engineering: Improving Practice
through Research" (B. Hnatkowska and M. \'Smia{\l}ek, eds.), pp. 131-146,
201
Connecting Software Metrics across Versions to Predict Defects
Accurate software defect prediction could help software practitioners
allocate test resources to defect-prone modules effectively and efficiently. In
the last decades, much effort has been devoted to build accurate defect
prediction models, including developing quality defect predictors and modeling
techniques. However, current widely used defect predictors such as code metrics
and process metrics could not well describe how software modules change over
the project evolution, which we believe is important for defect prediction. In
order to deal with this problem, in this paper, we propose to use the
Historical Version Sequence of Metrics (HVSM) in continuous software versions
as defect predictors. Furthermore, we leverage Recurrent Neural Network (RNN),
a popular modeling technique, to take HVSM as the input to build software
prediction models. The experimental results show that, in most cases, the
proposed HVSM-based RNN model has a significantly better effort-aware ranking
effectiveness than the commonly used baseline models
Too Trivial To Test? An Inverse View on Defect Prediction to Identify Methods with Low Fault Risk
Background. Test resources are usually limited and therefore it is often not
possible to completely test an application before a release. To cope with the
problem of scarce resources, development teams can apply defect prediction to
identify fault-prone code regions. However, defect prediction tends to low
precision in cross-project prediction scenarios.
Aims. We take an inverse view on defect prediction and aim to identify
methods that can be deferred when testing because they contain hardly any
faults due to their code being "trivial". We expect that characteristics of
such methods might be project-independent, so that our approach could improve
cross-project predictions.
Method. We compute code metrics and apply association rule mining to create
rules for identifying methods with low fault risk. We conduct an empirical
study to assess our approach with six Java open-source projects containing
precise fault data at the method level.
Results. Our results show that inverse defect prediction can identify approx.
32-44% of the methods of a project to have a low fault risk; on average, they
are about six times less likely to contain a fault than other methods. In
cross-project predictions with larger, more diversified training sets,
identified methods are even eleven times less likely to contain a fault.
Conclusions. Inverse defect prediction supports the efficient allocation of
test resources by identifying methods that can be treated with less priority in
testing activities and is well applicable in cross-project prediction
scenarios.Comment: Submitted to PeerJ C
Simulation of radiation-induced defects
Mainly due to their outstanding performance the position sensitive silicon
detectors are widely used in the tracking systems of High Energy Physics
experiments such as the ALICE, ATLAS, CMS and LHCb at LHC, the world's largest
particle physics accelerator at CERN, Geneva. The foreseen upgrade of the LHC
to its high luminosity (HL) phase (HL-LHC scheduled for 2023), will enable the
use of maximal physics potential of the facility. After 10 years of operation
the expected fluence will expose the tracking systems at HL-LHC to a radiation
environment that is beyond the capacity of the present system design. Thus, for
the required upgrade of the all-silicon central trackers extensive measurements
and simulation studies for silicon sensors of different designs and materials
with sufficient radiation tolerance have been initiated within the RD50
Collaboration.
Supplementing measurements, simulations are in vital role for e.g. device
structure optimization or predicting the electric fields and trapping in the
silicon sensors. The main objective of the device simulations in the RD50
Collaboration is to develop an approach to model and predict the performance of
the irradiated silicon detectors using professional software. The first
successfully developed quantitative models for radiation damage, based on two
effective midgap levels, are able to reproduce the experimentally observed
detector characteristics like leakage current, full depletion voltage and
charge collection efficiency (CCE). Recent implementations of additional traps
at the SiO/Si interface or close to it have expanded the scope of the
experimentally agreeing simulations to such surface properties as the
interstrip resistance and capacitance, and the position dependency of CCE for
strip sensors irradiated up to
n.Comment: 13 pages, 11 figures, 6 tables, 24th International Workshop on Vertex
Detectors, 1-5 June 2015, Santa Fe, New Mexico, US
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