237 citations · 2.5k across the 105 of their papers we have counts for
15 papers · 1 filter
Early Life Cycle Software Defect Prediction. Why? How?
N. C. Shrikanth, Suvodeep Majumder, Tim Menzies
Many researchers assume that, for software analytics, "more data is better." We write to show that, at least for learning defect predictors, this may not be true. To demonstrate th…
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack
Rui Shu, Tianpei Xia, Laurie Williams +1
Background: Machine learning-based security detection models have become prevalent in modern malware and intrusion detection systems. However, previous studies show that such model…
Making Fair ML Software using Trustworthy Explanation
Joymallya Chakraborty, Kewen Peng, Tim Menzies
Machine learning software is being used in many applications (finance, hiring, admissions, criminal justice) having a huge social impact. But sometimes the behavior of this softwar…
Revisiting Process versus Product Metrics: a Large Scale Analysis
Suvodeep Majumder, Pranav Mody, Tim Menzies
Numerous methods can build predictive models from software data. However, what methods and conclusions should we endorse as we move from analytics in-the-small (dealing with a hand…
Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?
Amritanshu Agrawal, Xueqi Yang, Rishabh Agrawal +2
How to make software analytics simpler and faster? One method is to match the complexity of analysis to the intrinsic complexity of the data being explored. For example, hyperparam…
On the Value of Oversampling for Deep Learning in Software Defect Prediction
Rahul Yedida, Tim Menzies
One truism of deep learning is that the automatic feature engineering (seen in the first layers of those networks) excuses data scientists from performing tedious manual feature en…
How Different is Test Case Prioritization for Open and Closed Source Projects?
Xiao Ling, Rishabh Agrawal, Tim Menzies
Improved test case prioritization means that software developers can detect and fix more software faults sooner than usual. But is there one "best" prioritization algorithm? Or do…
Defect Reduction Planning (using TimeLIME)
Kewen Peng, Tim Menzies
Software comes in releases. An implausible change to software is something that has never been changed in prior releases. When planning how to reduce defects, it is better to use p…
Predicting Health Indicators for Open Source Projects (using Hyperparameter Optimization)
Tianpei Xia, Wei Fu, Rui Shu +2
Software developed on public platform is a source of data that can be used to make predictions about those projects. While the individual developing activity may be random and hard…
Assessing Practitioner Beliefs about Software Engineering
N. C. Shrikanth, William Nichols, Fahmid Morshed Fahid +1
Software engineering is a highly dynamic discipline. Hence, as times change, so too might our beliefs about core processes in this field. This paper checks some five beliefs that o…
Learning to Recognize Actionable Static Code Warnings (is Intrinsically Easy)
Xueqi Yang, Jianfeng Chen, Rahul Yedida +2
Static code warning tools often generate warnings that programmers ignore. Such tools can be made more useful via data mining algorithms that select the "actionable" warnings; i.e.…
How to Improve AI Tools (by Adding in SE Knowledge): Experiments with the TimeLIME Defect Reduction Tool
Kewen Peng, Tim Menzies
AI algorithms are being used with increased frequency in SE research and practice. Such algorithms are usually commissioned and certified using data from outside the SE domain. Can…
The Changing Nature of Computational Science Software
Huy Tu, Rishabh Agrawal, Tim Menzies
How should software engineering be adapted for Computational Science (CS)? If we understood that, then we could better support software sustainability, verifiability, reproducibili…
Fairway: A Way to Build Fair ML Software
Joymallya Chakraborty, Suvodeep Majumder, Zhe Yu +1
Machine learning software is increasingly being used to make decisions that affect people's lives. But sometimes, the core part of this software (the learned model), behaves in a b…
Identifying Self-Admitted Technical Debts with Jitterbug: A Two-step Approach
Zhe Yu, Fahmid Morshed Fahid, Huy Tu +1
Keeping track of and managing Self-Admitted Technical Debts (SATDs) are important to maintaining a healthy software project. This requires much time and effort from human experts t…