Related papers: Researcher Bias in Software Engineering Experiment…
This paper describes REALab, a platform for embedded agency research in reinforcement learning (RL). REALab is designed to model the structure of tampering problems that may arise in real-world deployments of RL. Standard Markov Decision…
This study inserts in the stream of research on the perverse effects that PBRF systems can induce in the subjects evaluated. The authors' opinion is that more often than not, it is the doubtful scientific basis of the evaluation criteria…
Randomized benchmarking (RB) protocols are widely used to measure an average error rate for a set of quantum logic gates. However, the standard version of RB is limited because it only benchmarks a processor's native gates indirectly, by…
The computing research community needs to work much harder to address the downsides of our innovations. Between the erosion of privacy, threats to democracy, and automation's effect on employment (among many other issues), we can no longer…
Requirements Engineering (RE)-related activities require high collaboration between various roles in software engineering (SE), such as requirements engineers, stakeholders, developers, etc. Their demographics, views, understanding of…
It is standard practice in online retail to run pricing experiments by randomizing at the article-level, i.e. by changing prices of different products to identify treatment effects. Due to customers' cross-price substitution behavior, such…
This paper presents Bayesian techniques for conservative claims about software reliability, particularly when evidence suggests the software's executions are not statistically independent. We formalise informal notions of "doubting" that…
Sentiment analysis (SA) systems, though widely applied in many domains, have been demonstrated to produce biased results. Some research works have been done in automatically generating test cases to reveal unfairness in SA systems, but the…
We study masked implementation's security when an adversary randomly probes each of its internal variables, intending to recover non-trivial knowledge about its secrets. We introduce a novel metric called Secret Recovery Probability (SRP)…
Software is now a vital scientific instrument, providing the tools for data collection and analysis across disciplines from bioinformatics and computational physics, to the humanities. The software used in research is often home-grown and…
Software security has been an important research topic over the years. The community has proposed processes and tools for secure software development and security analysis. However, a significant number of vulnerabilities remains in…
Unsupervised speech emotion recognition (SER) focuses on addressing the problem of data sparsity and annotation bias of emotional speech. Reinforcement learning (RL) is a promising method which enhances the performance through rule-based or…
Context: The Impostor Phenomenon (IP), the persistent belief of being a fraud despite evident competence, is common in Software Engineering (SE), where high expectations for expertise and innovation prevail. Although coaching and similar…
The field of software engineering is embedded in both engineering and computer science, and may embody gender biases endemic to both. This paper surveys software engineering's origins and its long-running attention to engineering…
Reinforcement Learning (RL) is a computational approach to reward-driven learning in sequential decision problems. It implements the discovery of optimal actions by learning from an agent interacting with an environment rather than from…
Context: Software practitioners are a primary provider of information for field studies in software engineering. Research typically recruits practitioners through some kind of sampling. But sampling may not in itself recruit credible…
The field of deep learning has witnessed significant breakthroughs, spanning various applications, and fundamentally transforming current software capabilities. However, alongside these advancements, there have been increasing concerns…
Financial markets are influenced by human behavior that deviates from rationality due to cognitive biases. Traditional reinforcement learning (RL) models for financial decision-making assume rational agents, potentially overlooking the…
Two-sided platforms are central to modern commerce and content sharing and often utilize A/B testing for developing new features. While user-side experiments are common, seller-side experiments become crucial for specific interventions and…
Randomized benchmarking (RB) is the gold standard for experimentally evaluating the quality of quantum operations. The current framework for RB is centered on groups and their representations, but this can be problematic. For example,…