1,721,078 research outputs found
Replication Package: Using Voice and Biofeedbackto Predict User Engagementduring Requirements Interviews
No full textThis is a replication package for the paper "Using Voice and Biofeedbackto Predict User Engagementduring Requirements Interviews", by Alessio Ferrari, Thaide Huichapa, Paola Spoletini, Nicole Novielli, Davide Fucci and Daniela Girardi
Few-Shot Learning for Issue Report Classification
No full textWe describe our participation in the tool competition in the scope of the 2nd International Workshop on Natural Language-based Software Engineering. We propose a supervised approach relying on SETFIT, a framework for few-shot learning and sentence-BERT (SBERT), a variant of BERT for effective sentence embedding. We experimented with different settings, achieving the best performance by training and testing the SETFIT-based model on a subset of data with manually verified labels (Fl-micro =.8321 ). For the sake of the challenge, we evaluate the SETFIT model on the challenge test set, achieving Fl-micro =.7767
Sensor-Based Emotion Recognition in Software Development: Facial Expressions as Gold Standard
No full textEarly identification of emotions of software developers can enable timely intervention in order to support developers' well-being and prevent burnout. We present a machine learning experiment aimed at recognizing emotions during programming tasks using wearable biometric sensors, tracking electrodermal activity and heart-related metrics.
As a gold standard for supervised learning, we rely on a state-of-the-art tool for emotion recognition based on facial expression analysis. We design, implement and evaluate an approach that combines the output of two classifiers for neutral valence recognition and positive/negative polarity classification.
Our findings suggest that biometric sensors in a wristband can be used to identify emotions whose recognition would otherwise need an intrusive webcam
A Cluster-based Approach for Emotion Recognition in Software Development
No full textTimely recognizing developers’ emotions is crucial to support their productivity and well-being. To this aim, recent research has proposed approaches for sensor-based emotion detection during programming tasks, with promising results. However, limitations exist due to individual physiological differences, which we aim to address in the current study. Specifically, we propose a novel cluster-based approach for emotion recognition in software development using non-invasive biometric sensors. Our methodology enables the training of emotion recognition models specific to groups of people with similar physiological profiles. We evaluate our approach using a dataset of self-reported emotions and biometrics of developers involved in a Java programming task. Results show that our cluster-based approach enhances the performance of emotion classification compared to baseline approaches that do not account for individual differences. For valence recognition, we achieve improvements in precision (+33%), recall (+12%), and F1-score (+18%). For arousal, the most notable improvement is observed in precision (+29%). The classifier demonstrates particular effectiveness in identifying negative emotions with high arousal, which is especially valuable for detecting emotional states potentially detrimental to developers’ well-being. While promising, our results suggest that further optimization through additional data collection and individual model training would be beneficial before practical deployment.
The study includes a comprehensive evaluation of the proposed approach and provides a lab package for replication purposes
Exploring Engagement in Hybrid Meetings
No full textBackground. The widespread adoption of hybrid work following the COVID-19 pandemic has fundamentally transformed software development practices, introducing new challenges in communication and collaboration as organizations transition from traditional office-based structures to flexible working arrangements. This shift has established a new orga- nizational norm where even traditionally office-first companies now embrace hybrid team structures. While remote participation in meetings has become commonplace in this new environment, it may lead to isolation, alienation, and decreased engagement among remote team members. Aims. This study aims to iden- tify and characterize engagement patterns in hybrid meetings through objective measurements, focusing on the differences between co-located and remote participants. Method. We studied professionals from three software companies over several weeks, employing a multimodal approach to measure engagement. Data were collected through self-reported questionnaires and phys- iological measurements using biometric devices during hybrid meetings to understand engagement dynamics. Results. The re- gression analyses revealed comparable engagement levels between onsite and remote participants, though remote participants show lower engagement in long meetings regardless of participation mode. Active roles positively correlate with higher engagement, while larger meetings and afternoon sessions are associated with lower engagement. Conclusions. Our results offer insights into factors associated with engagement and disengagement in hybrid meetings, as well as potential meeting improvement recommenda- tions. These insights are potentially relevant not only for software teams but also for knowledge-intensive organizations across various sectors facing similar hybrid collaboration challenges
Predicting Bugs by Monitoring Developers During Task Execution
Get PDFKnowing which parts of the source code will be defective can allow practitioners to better allocate testing resources.
For this reason, many approaches have been proposed to achieve this goal. Most state-of-the-art predictive models rely on product
and process metrics, i.e., they predict the defectiveness of a component by considering what developers did. However, there is still limited evidence of the benefits that can be achieved in this context by monitoring how developers complete a development task. In this paper, we present an empirical study in which we aim at understanding whether measuring human aspects on developers while they write code can help predict the introduction of defects.
First, we introduce a new developer-based model which relies on behavioral, psychophysical, and control factors that can be measured during the execution of development tasks. Then, we run a controlled experiment involving 20 software developers to understand if our developer-based model is able to predict the introduction of bugs. Our results show that a developed-based model is able to achieve a similar accuracy compared to a state-of-the-art code-based model, i.e., a model that uses only features measured from the source code. We also observed that by combining the models it is possible to obtain the best results (∼84% accuracy)
More Than Code: Technical and Emotional Dynamics in Solidity’s Development
No full textBackground: Solidity is the primary programming language used for developing smart contracts on Ethereum, representing a new generation of programming languages developed entirely in open environments.
Objective: This longitudinal case study examines contribution patterns and emotional dynamics within the Solidity GitHub repository over a ten-year period (2014-2024).
Method: We developed a contribution index combining metrics from developer activities (commits, pull requests, comments, and temporal engagement) and applied emotion detection to study communication patterns in a decade-long dataset of developer interactions.
Results: The top 1% of contributors are responsible for around 85% of project contributions, yet the project exhibits dual paths to prominence: early contributors established technical foundations through code, while later contributors achieved influence through reviews and discussions. Emotional patterns show transitions from initial curiosity and confusion to eventual approval and gratitude.
Conclusion: The project’s recognition of diverse contribution types and evolving emotional dynamics enables sustainable growth despite concentrated contributions, demonstrating how open-source languages can evolve while maintaining both technical rigor and community engagemen
Can We Use SE-specific Sentiment Analysis Tools in a Cross-Platform Setting?
Get PDFIn this paper, we address the problem of using sentiment analysis tools ‘off-the-shelf’, that is when a gold standard is not available for retraining. We evaluate the performance of four SE-specific tools in a cross-platform setting, i.e., on a test set collected from data sources different from the one used for training. We find that (i) the lexicon-based tools outperform the supervised approaches retrained in a cross-platform setting and (ii) retraining can be beneficial in within-platform settings in the presence of robust gold standard datasets, even using a minimal training set. Based on our empirical findings, we derive guidelines for reliable use of sentiment analysis tools in software engineering
Proceedings of the 9th Italian Conference on Computational Linguistics CLiC-it 2023
No full textProceedings of the 9th Italian Conference on Computational Linguistics (CLiC-it 2023), held in Venice from November 30 to December 2, 2023
A Preliminary Analysis on the Effects of Propensity to Trust in Distributed Software Development
No full textEstablishing trust between developers working at distant sites facilitates team collaboration in distributed software development. While previous research has focused on how to build and spread trust in absence of direct, face-to-face communication, it has overlooked the effects of the propensity to trust, i.e., the trait of personality representing the individual disposition to perceive the others as trustworthy. In this study, we present a preliminary, quantitative analysis on how the propensity to trust affects the success of collaborations in a distributed project, where the success is represented by pull requests whose code changes and contributions are successfully merged into the project's repository
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