Search Results for author:
Found 47 papers, 25 papers with code
Mining Temporal Attack Patterns from Cyberthreat Intelligence Reports
The goal of this paper is to aid security practitioners in prioritizing and proactive defense against cyberattacks by mining temporal attack patterns from cyberthreat intelligence reports.
Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics
Standard SSL algorithms use ``weak'' knowledge (i. e. those not based on specific SE knowledge) such as (e. g.) co-train two learners and use good labels from one to train the other.
Don't Lie to Me: Avoiding Malicious Explanations with STEALTH
STEALTH is a method for using some AI-generated model, without suffering from malicious attacks (i. e. lying) or associated unfairness issues.
Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health
For example, for project health indicators such as $C$= number of commits; $I$=number of closed issues, and $R$=number of closed pull requests, niSNEAK's 12 month prediction errors are \{I=0\%, R=33\%\, C=47\%\} Based on the above, we recommend landscape analytics (e. g. niSNEAK) especially when learning from very small data sets.
A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning
Deep neural networks (DNNs), are widely used in many industries such as image recognition, supply chain, medical diagnosis, and autonomous driving.
When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors
Hence, there are often limits on how much-labeled data is available for training.
How to Find Actionable Static Analysis Warnings: A Case Study with FindBugs
Automatically generated static code warnings suffer from a large number of false alarms.
Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue
Conclusion: Based on this study, we would suggest the use of optimized GANs as an alternative method for security vulnerability data class imbalanced issues.
DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning
The human experts are then required to read almost a quintuple of the SATD comments which indicates the inefficiency of the tool.
Fair Enough: Searching for Sufficient Measures of Fairness
In summary, to simplify the fairness testing problem, we recommend the following steps: (1)~determine what type of fairness is desirable (and we offer a handful of such types); then (2) lookup those types in our clusters; then (3) just test for one item per cluster.
FairMask: Better Fairness via Model-based Rebalancing of Protected Attributes
Our approach aims to offset the biased predictions of the classification model via rebalancing the distribution of protected attributes.
An Expert System for Redesigning Software for Cloud Applications
When services are divided into many independent components, they are easier to update.
FRUGAL: Unlocking SSL for Software Analytics
However, prior work has shown that such requirements can be expensive, taking several weeks to label thousands of commits, and not always available when traversing new research problems and domains.
FairBalance: How to Achieve Equalized Odds With Data Pre-processing
We found that equalizing the class distribution in each demographic group with sample weights is a necessary condition for achieving equalized odds without modifying the normal training process.
Bias in Machine Learning Software: Why? How? What to do?
This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those examples.
Assessing the Early Bird Heuristic (for Predicting Project Quality)
Moreover, using this early bird method, we have shown that a simple model (with just a few features) generalizes to hundreds of projects.
Old but Gold: Reconsidering the value of feedforward learners for software analytics
We test the hypothesis laid by Galke and Scherp [18], that feedforward networks suffice for many analytics tasks (which we call, the "Old but Gold" hypothesis) for these two tasks.
Early Life Cycle Software Defect Prediction. Why? How?
Hence, defect predictors learned from the first 150 commits and four months perform just as well as anything else.
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack
Conclusion: When employing ensemble defense against adversarial evasion attacks, we suggest creating an ensemble with unexpected models that are distant from the attacker's expected model (i. e., target model) through methods such as hyperparameter optimization.
Empirical Standards for Software Engineering Research
Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e. g. a questionnaire survey).
Software Engineering General Literature
Revisiting Process versus Product Metrics: a Large Scale Analysis
We find that some analytics in-the-small conclusions still hold when scaling up to analytics in-the-large.
How Different is Test Case Prioritization for Open and Closed Source Projects?
Improved test case prioritization means that software developers can detect and fix more software faults sooner than usual.
Software Engineering
Identifying Self-Admitted Technical Debts with Jitterbug: A Two-step Approach
Keeping track of and managing the self-admitted technical debts (SATDs) is important to maintaining a healthy software project.
Software Engineering
Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?
How can we make software analytics simpler and faster?
Methods for Stabilizing Models across Large Samples of Projects (with case studies on Predicting Defect and Project Health)
To the best of our knowledge, STABILIZER is order of magnitude faster than the prior state-of-the-art transfer learners which seek to find conclusion stability, and these case studies are the largest demonstration of the generalizability of quantitative predictions of project quality yet reported in the SE literature.
How to Better Distinguish Security Bug Reports (using Dual Hyperparameter Optimization
For example, in a study of security bug reports from the Chromium dataset, the median recalls of FARSEC and Swift were 15. 7% and 77. 4%, respectively.
Software Engineering
Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE
To resolve these problems, we propose a novel transfer learning framework called BEETLE, which is a "bellwether"-based transfer learner that focuses on identifying and learning from the most relevant source from amongst the old data.
Software Engineering
Better Technical Debt Detection via SURVEYing
Specifically, for ten open-source JAVA projects, we can find 83% of the technical debt via SURVEY0 using just 16% of the comments (and if higher levels of recall are required, SURVEY0can adjust towards that with some additional effort).
TERMINATOR: Better Automated UI Test Case Prioritization
Given that much of the automated UI testing is "black box" in nature, very little information (only the test case descriptions and testing results) can be utilized to prioritize these automated UI test cases.
Software Engineering for Fairness: A Case Study with Hyperparameter Optimization
To the best of our knowledge, this is the first application of hyperparameter optimization as a tool for software engineers to generate fairer software.
How to "DODGE" Complex Software Analytics?
Machine learning techniques applied to software engineering tasks can be improved by hyperparameter optimization, i. e., automatic tools that find good settings for a learner's control parameters.
Hyperparameter Optimization for Effort Estimation
We test OIL on a wide range of hyperparameter optimizers using data from 945 software projects.
Software Engineering
Transfer Learning with Bellwethers to find Good Configurations
Using this insight, this paper proposes BEETLE, a novel bellwether based transfer learning scheme, which can identify a suitable source and use it to find near-optimal configurations of a software system.
Software Engineering
500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow)
Deep learners utilizes extensive computational power and can take a long time to train-- making it difficult to widely validate and repeat and improve their results.
Finding Faster Configurations using FLASH
FLASH scales up to software systems that defeat the prior state of the art model-based methods in this area.
Software Engineering
RIOT: a Stochastic-based Method for Workflow Scheduling in the Cloud
Cloud computing provides engineers or scientists a place to run complex computing tasks.
Learning Effective Changes For Software Projects
The current generation of software analytics tools are mostly prediction algorithms (e. g. support vector machines, naive bayes, logistic regression, etc).
Software Engineering
Revisiting Unsupervised Learning for Defect Prediction
(1) There is much variability in the efficacy of the Yang et al. predictors so even with their approach, some supervised data is required to prune weaker predictors away.
Easy over Hard: A Case Study on Deep Learning
While deep learning is an exciting new technique, the benefits of this method need to be assessed with respect to its computational cost.
Faster Discovery of Faster System Configurations with Spectral Learning
Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations.
Beyond Evolutionary Algorithms for Search-based Software Engineering
Context: Evolutionary algorithms typically require a large number of evaluations (of solutions) to converge - which can be very slow and expensive to evaluate. Objective: To solve search-based software engineering (SE) problems, using fewer evaluations than evolutionary methods. Method: Instead of mutating a small population, we build a very large initial population which is then culled using a recursive bi-clustering chop approach.
Finding Better Active Learners for Faster Literature Reviews
Literature reviews can be time-consuming and tedious to complete.
Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?
In software analytics, at least for defect prediction, several methods, like grid search and differential evolution (DE), have been proposed to learn these parameters, which has been proved to be able to improve the performance scores of learners.
A deep learning model for estimating story points
Although there has been substantial research in software analytics for effort estimation in traditional software projects, little work has been done for estimation in agile projects, especially estimating user stories or issues.
What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)
When run on different datasets, LDA suffers from "order effects" i. e. different topics are generated if the order of training data is shuffled.
