Recognizing Developers' Emotions while Programming

Developers experience a wide range of emotions during programming tasks, which may have an impact on job performance. In this paper, we present an empirical study aimed at (i) investigating the link between emotion and progress, (ii) understanding the triggers for developers' emotions and the strategies to deal with negative ones, (iii) identifying the minimal set of non-invasive biometric sensors for emotion recognition during programming task. Results confirm previous findings about the relation between emotions and perceived productivity. Furthermore, we show that developers' emotions can be reliably recognized using only a wristband capturing the electrodermal activity and heart-related metrics.Comment: Accepted for publication at ICSE2020 Technical Trac

Software System for Vocal Rendering of Printed Documents

The objective of this paper is to present a software system architecture developed to render the printed documents in a vocal form. On the other hand, in the paper are described the software solutions that exist as software components and are necessary for documents processing as well as for multimedia device controlling used by the system. The usefulness of this system is for people with visual disabilities that can access the contents of documents without that they be printed in Braille system or to exist in an audio form.accessibility, TWAIN, OCR, TTS, SAPI

Artificial Intelligence in the Context of Human Consciousness

Artificial intelligence (AI) can be defined as the ability of a machine to learn and make decisions based on acquired information. AI’s development has incited rampant public speculation regarding the singularity theory: a futuristic phase in which intelligent machines are capable of creating increasingly intelligent systems. Its implications, combined with the close relationship between humanity and their machines, make achieving understanding both natural and artificial intelligence imperative. Researchers are continuing to discover natural processes responsible for essential human skills like decision-making, understanding language, and performing multiple processes simultaneously. Artificial intelligence attempts to simulate these functions through techniques like artificial neural networks, Markov Decision Processes, Human Language Technology, and Multi-Agent Systems, which rely upon a combination of mathematical models and hardware

On negative results when using sentiment analysis tools for software engineering research

Recent years have seen an increasing attention to social aspects of software engineering, including studies of emotions and sentiments experienced and expressed by the software developers. Most of these studies reuse existing sentiment analysis tools such as SentiStrength and NLTK. However, these tools have been trained on product reviews and movie reviews and, therefore, their results might not be applicable in the software engineering domain. In this paper we study whether the sentiment analysis tools agree with the sentiment recognized by human evaluators (as reported in an earlier study) as well as with each other. Furthermore, we evaluate the impact of the choice of a sentiment analysis tool on software engineering studies by conducting a simple study of differences in issue resolution times for positive, negative and neutral texts. We repeat the study for seven datasets (issue trackers and Stack Overflow questions) and different sentiment analysis tools and observe that the disagreement between the tools can lead to diverging conclusions. Finally, we perform two replications of previously published studies and observe that the results of those studies cannot be confirmed when a different sentiment analysis tool is used

Emotion And Cognition Analysis Of Intro And Senior CS Students In Software Engineering

he software engineering community has advanced the field in the past few decades towards making the software development life cycle more efficient, robust, and streamlined. Advances such as better integrated development environments and agile workflows have made the process more efficient as well as more flexible. Despite these many achievements software engineers still spend a great deal of time writing, reading and reviewing code. These tasks require a lot of attention from the engineer with many different variables affecting the performance of the tasks. In recent years many researchers have come to investigate how emotion and the way we think about code affect our ability to write and understand another’s code. In this work we look at how developers’ emotions affect their ability to solve software engineering tasks such as code writing and review. We also investigate how and to what extent emotions differ with the software engineering experience of the subject. The methodologies we employed utilize the Emotiv Epoc+ to take readings of subjects’ brain patterns while they perform code reviews as well as write basic code. We then examine how the electrical signals and patterns in the participants differ with experience in the field, as well as their efficiency and correctness in solving the software engineering tasks. We found in our study that senior students had much smaller distribution of emotions than novices with a few different emotion groups emerging. The novices, while able to be grouped, had a much wider dispersion of the emotion aspects recorded

Designing Women: Essentializing Femininity in AI Linguistics

Since the eighties, feminists have considered technology a force capable of subverting sexism because of technology’s ability to produce unbiased logic. Most famously, Donna Haraway’s “A Cyborg Manifesto” posits that the cyborg has the inherent capability to transcend gender because of its removal from social construct and lack of loyalty to the natural world. But while humanoids and artificial intelligence have been imagined as inherently subversive to gender, current artificial intelligence perpetuates gender divides in labor and language as their programmers imbue them with traits considered “feminine.” A majority of 21st century AI and humanoids are programmed to fit female stereotypes as they fulfill emotional labor and perform pink-collar tasks, whether through roles as therapists, query-fillers, or companions. This paper examines four specific chat-based AI --ELIZA, XiaoIce, Sophia, and Erica-- and examines how their feminine linguistic patterns are used to maintain the illusion of emotional understanding in regards to the tasks that they perform. Overall, chat-based AI fails to subvert gender roles, as feminine AI are relegated to the realm of emotional intelligence and labor