237 citations · 2.5k across the 105 of their papers we have counts for
11 papers · 1 filter
Impacts of Bad ESP (Early Size Predictions) on Software Effort Estimation
George Mathew, Tim Menzies, Jairus Hihn
Context: Early size predictions (ESP) can lead to errors in effort predictions for software projects. This problem is particular acute in parametric effort models that give extra w…
Finding Better Active Learners for Faster Literature Reviews
Zhe Yu, Nicholas A. Kraft, Tim Menzies
Literature reviews can be time-consuming and tedious to complete. By cataloging and refactoring three state-of-the-art active learning techniques from evidence-based medicine and l…
Negative Results for Software Effort Estimation
Tim Menzies, Ye Yang, George Mathew +2
Context:More than half the literature on software effort estimation (SEE) focuses on comparisons of new estimation methods. Surprisingly, there are no studies comparing state of th…
Are Delayed Issues Harder to Resolve? Revisiting Cost-to-Fix of Defects throughout the Lifecycle
Tim Menzies, William Nichols, Forrest Shull +1
Many practitioners and academics believe in a delayed issue effect (DIE); i.e. the longer an issue lingers in the system, the more effort it requires to resolve. This belief is oft…
Tuning for Software Analytics: is it Really Necessary?
Wei Fu, Tim Menzies, Xipeng Shen
Context: Data miners have been widely used in software engineering to, say, generate defect predictors from static code measures. Such static code defect predictors perform well co…
A deep learning model for estimating story points
Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran +3
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 project…
Less is More: Minimizing Code Reorganization using XTREE
Rahul Krishna, Tim Menzies, Lucas Layman
Context: Developers use bad code smells to guide code reorganization. Yet developers, text books, tools, and researchers disagree on which bad smells are important. Objective: To e…
Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?
Wei Fu, Vivek Nair, Tim Menzies
Context: One of the black arts of data mining is learning the magic parameters which control the learners. In software analytics, at least for defect prediction, several methods, l…
What is Wrong with Topic Modeling? (and How to Fix it Using Search-based Software Engineering)
Amritanshu Agrawal, Wei Fu, Tim Menzies
Context: Topic modeling finds human-readable structures in unstructured textual data. A widely used topic modeler is Latent Dirichlet allocation. When run on different datasets, LD…
Finding Trends in Software Research
George Mathew, Amritanshu Agrawal, Tim Menzies
This paper explores the structure of research papers in software engineering. Using text mining, we study 35,391 software engineering (SE) papers from 34 leading SE venues over the…
"Sampling"' as a Baseline Optimizer for Search-based Software Engineering
Jianfeng Chen, Vivek Nair, Rahul Krishna +1
Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply…