The impact of automated parameter optimization on defect prediction models
C Tantithamthavorn, S McIntosh… - IEEE Transactions …, 2018 - ieeexplore.ieee.org
Defect prediction models-classifiers that identify defect-prone software modules-have
configurable parameters that control their characteristics (eg, the number of trees in a …
configurable parameters that control their characteristics (eg, the number of trees in a …
A survey on machine learning techniques for source code analysis
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
On the application of search-based techniques for software engineering predictive modeling: A systematic review and future directions
Software engineering predictive modeling involves construction of models, with the help of
software metrics, for estimating quality attributes. Recently, the use of search-based …
software metrics, for estimating quality attributes. Recently, the use of search-based …
The importance of accounting for real-world labelling when predicting software vulnerabilities
Previous work on vulnerability prediction assume that predictive models are trained with
respect to perfect labelling information (includes labels from future, as yet undiscovered …
respect to perfect labelling information (includes labels from future, as yet undiscovered …
Towards building a universal defect prediction model with rank transformed predictors
Software defects can lead to undesired results. Correcting defects costs 50% to 75% of the
total software development budgets. To predict defective files, a prediction model must be …
total software development budgets. To predict defective files, a prediction model must be …
Hyper-parameter optimization of classifiers, using an artificial immune network and its application to software bug prediction
Software testing is an important task in software development activities, and it requires most
of the resources, namely, time, cost and effort. To minimize this fatigue, software bug …
of the resources, namely, time, cost and effort. To minimize this fatigue, software bug …
[HTML][HTML] A survey on machine learning techniques applied to source code
The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …
these techniques to a myriad of software engineering tasks that use source code analysis …
Linear programming as a baseline for software effort estimation
F Sarro, A Petrozziello - ACM transactions on software engineering and …, 2018 - dl.acm.org
Software effort estimation studies still suffer from discordant empirical results (ie, conclusion
instability) mainly due to the lack of rigorous benchmarking methods. So far only one …
instability) mainly due to the lack of rigorous benchmarking methods. So far only one …
Cross-version defect prediction: use historical data, cross-project data, or both?
S Amasaki - Empirical Software Engineering, 2020 - Springer
Context Although a long-running project has experienced many releases, removing defects
from a product is still a challenge. Cross-version defect prediction (CVDP) regards project …
from a product is still a challenge. Cross-version defect prediction (CVDP) regards project …
How to “dodge” complex software analytics
Machine learning techniques applied to software engineering tasks can be improved by
hyperparameter optimization, ie, automatic tools that find good settings for a learner's control …
hyperparameter optimization, ie, automatic tools that find good settings for a learner's control …