Tim Menzies

Using Stakeholder Preferences to Make Better Architecture Decisions

2017

A roadmap to modernize the architecture of an existing system must satisfy many strongly-positioned stakeholders and satisfy the constraints of continuing business operations as the plan is implemented. Our previous work reported on a method …

FAST$^2$: Better Automated Support for Finding Relevant SE Research Papers

2017 · arXiv (Cornell University)

Literature reviews are essential for any researcher trying to keep up to date with the burgeoning software engineering literature. FAST$^2$ is a novel tool for reducing the effort required for conducting literature reviews by assisting …

Hyperparameter Optimization for Effort Estimation

2018 · arXiv (Cornell University)

Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data …

"Sampling"' as a Baseline Optimizer for Search-based Software Engineering

2016 · arXiv (Cornell University)

Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. …

The Changing Nature of Computational Science Software

2020 · arXiv (Cornell University)

How should software engineering be adapted for Computational Science (CS)? If we understood that, then we could better support software sustainability, verifiability, reproducibility, comprehension, and usability for CS community. For example, improving the maintainability of …

Documenting evidence of a reuse of ‘what is a feature? a qualitative study of features in industrial software product lines’

2021

We report here the following example of reuse. The original paper is a prior work about features in product lines by Berger et al. The paper "Dimensions of software configuration: on the configuration context in …

Documenting evidence of a replication of ‘populating a release history database from version control and bug tracking systems’

2021

We report here the use of a keyword-based and regular expression-based approach to identify bug-fixing commits by linking commit messages and issue tracker data in a recent FSE '20 paper by Penta et al. in …

DebtFree: Minimizing Labeling Cost in Self-Admitted Technical Debt Identification using Semi-Supervised Learning

2022 · arXiv (Cornell University)

Keeping track of and managing Self-Admitted Technical Debts (SATDs) is important for maintaining a healthy software project. Current active-learning SATD recognition tool involves manual inspection of 24% of the test comments on average to reach …

Revisiting Process versus Product Metrics: a Large Scale Analysis

2020 · arXiv (Cornell University)

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 handful of projects) to analytics in-the-large (dealing with hundreds …

Assessing Expert System-Assisted Literature Reviews With a Case Study

2019 · arXiv (Cornell University)

Given the large number of publications in software engineering, frequent literature reviews are required to keep current on work in specific areas. One tedious work in literature reviews is to find relevant studies amongst thousands …

Don't Lie to Me: Avoiding Malicious Explanations with STEALTH

2023 · arXiv (Cornell University)

STEALTH is a method for using some AI-generated model, without suffering from malicious attacks (i.e. lying) or associated unfairness issues. After recursively bi-clustering the data, STEALTH system asks the AI model a limited number of …

Is Hyper-Parameter Optimization Different for Software Analytics?

2024 · arXiv (Cornell University)

Yes. SE data can have "smoother" boundaries between classes (compared to traditional AI data sets). To be more precise, the magnitude of the second derivative of the loss function found in SE data is typically …