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We consider the predict-then-optimize paradigm for decision-making in which a practitioner (1) trains a supervised learning model on historical data of decisions, contexts, and rewards, and then (2) uses the resulting model to make future…
Context: This study explores how software professionals identify and address biases in AI systems within the software industry, focusing on practical knowledge and real-world applications. Goal: We aimed to understand the strategies…
Self-Admitted Technical Debt (SATD) refers to circumstances where developers use textual artifacts to explain why the existing implementation is not optimal. Past research in detecting SATD has focused on either identifying SATD…
Technical debt has become a well-known metaphor among software professionals, illustrating how shortcuts taken during development can accumulate and become a burden for software projects. In the traditional notion of technical debt,…
This white paper provides an overview of the topic of "technical debt" and presents an approach for managing technical debt in teams. The white paper is based on the results of my dissertation, which aimed to translate scientific findings…
Self-admitted technical debt (SATD) refers to comments in which developers explicitly acknowledge code issues, workarounds, or suboptimal solutions. SATD is known to significantly increase software maintenance effort. While extensive…
Programs are a kind of communication to both computers and people, hence as students are trained to write programs they need to learn to write well-designed, readable code rather than code that simply functions correctly. The difficulty in…
Access to quality education remains a global challenge, particularly in crisis-affected regions. This study examines the decline in students' mathematical proficiency and proposes an innovative Moodle-based testing system that incorporates…
Instruction Tuning involves finetuning a language model on a collection of instruction-formatted datasets in order to enhance the generalizability of the model to unseen tasks. Studies have shown the importance of balancing different task…
Reinforcement learning is increasingly used for code-centric tasks. These tasks include code generation, summarization, understanding, repair, testing, and optimization. This trend is growing faster with large language models and autonomous…
The feedback provided by current testing education tools about the deficiencies in a student's test suite either mimics industry code coverage tools or lists specific instructor test cases that are missing from the student's test suite.…
In order to track and comprehend the academic achievement of students, both private and public educational institutions devote a significant amount of resources and labour. One of the difficult issues that institutes deal with on a regular…
Decision-makers often deploy the best-performing treatment from a randomized experiment, creating a winner's curse: selection favors treatments whose observed outcomes are high partly because of statistical noise, so the na\"ive estimate of…
Context. Technical Debt (TD), defined as software constructs that are beneficial in the short term but may hinder future change, is a frequently used term in software development practice. Nevertheless, practitioners do not always fully…
Apprenticeship learning crucially depends on effectively learning rewards, and hence control policies from user demonstrations. Of particular difficulty is the setting where the desired task consists of a number of sub-goals with temporal…
Quantum computing is a rapidly growing field attracting the interest of both researchers and software developers. Supported by its numerous open-source tools, developers can now build, test, or run their quantum algorithms. Although the…
Context: Technical Debt (TD) can be paid back either by those that incurred it or by others. We call the former self-fixed TD, and it can be particularly effective, as developers are experts in their own code and are well-suited to fix the…
We study a stochastic budget-allocation problem over $K$ tasks. At each round $t$, the learner chooses an allocation $X_t \in \Delta_K$. Task $k$ succeeds with probability $F_k(X_{t,k})$, where $F_1,\dots,F_K$ are nondecreasing…
Score reliability is necessary for establishing a validity argument for an instrument, and is therefore highly important to investigate. Depending on the proposed instrument use and score interpretations, differing degrees of precision in…
Curriculum learning is a training strategy that sorts the training examples by some measure of their difficulty and gradually exposes them to the learner to improve the network performance. Motivated by our insights from implicit curriculum…