Deep learning based vulnerability detection: Are we there yet?
Automated detection of software vulnerabilities is a fundamental problem in software
security. Existing program analysis techniques either suffer from high false positives or false …
security. Existing program analysis techniques either suffer from high false positives or false …
Evolution of software development effort and cost estimation techniques: five decades study using automated text mining approach
Software development effort and cost estimation (SDECE) is one of the most important tasks
in the field of software engineering. A large number of research papers have been published …
in the field of software engineering. A large number of research papers have been published …
Bias in machine learning software: Why? how? what to do?
Increasingly, software is making autonomous decisions in case of criminal sentencing,
approving credit cards, hiring employees, and so on. Some of these decisions show bias …
approving credit cards, hiring employees, and so on. Some of these decisions show bias …
An empirical comparison of model validation techniques for defect prediction models
Defect prediction models help software quality assurance teams to allocate their limited
resources to the most defect-prone modules. Model validation techniques, such as-fold …
resources to the most defect-prone modules. Model validation techniques, such as-fold …
The impact of automated parameter optimization on defect prediction models
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 …
The impact of class rebalancing techniques on the performance and interpretation of defect prediction models
Defect models that are trained on class imbalanced datasets (ie, the proportion of defective
and clean modules is not equally represented) are highly susceptible to produce inaccurate …
and clean modules is not equally represented) are highly susceptible to produce inaccurate …
Automated parameter optimization of classification techniques for defect prediction models
Defect prediction models are classifiers that are trained to identify defect-prone software
modules. Such classifiers have configurable parameters that control their characteristics (eg …
modules. Such classifiers have configurable parameters that control their characteristics (eg …
Revisiting the impact of classification techniques on the performance of defect prediction models
Defect prediction models help software quality assurance teams to effectively allocate their
limited resources to the most defect-prone software modules. A variety of classification …
limited resources to the most defect-prone software modules. A variety of classification …
Effort-aware just-in-time defect prediction: simple unsupervised models could be better than supervised models
Unsupervised models do not require the defect data to build the prediction models and
hence incur a low building cost and gain a wide application range. Consequently, it would …
hence incur a low building cost and gain a wide application range. Consequently, it would …
Is" better data" better than" better data miners"? on the benefits of tuning SMOTE for defect prediction
We report and fix an important systematic error in prior studies that ranked classifiers for
software analytics. Those studies did not (a) assess classifiers on multiple criteria and they …
software analytics. Those studies did not (a) assess classifiers on multiple criteria and they …