相关论文: Understanding Fairness in Software Engineering: In…

Context: Software security patch management purports to support the process of patching known software security vulnerabilities. Given the increasing recognition of the importance of software security patch management, it is important and…

Building fair recommender systems is a challenging and crucial area of study due to its immense impact on society. We extended the definitions of two commonly accepted notions of fairness to recommender systems, namely equality of…

Many interesting problems in the Internet industry can be framed as a two-sided marketplace problem. Examples include search applications and recommender systems showing people, jobs, movies, products, restaurants, etc. Incorporating…

Context: Voluntary turnover is common in the software industry, increasing recruitment and onboarding costs and the risk of losing organizational and tacit knowledge. Objective: This study investigates how job satisfaction, work-life…

This paper presents the results of a research study related to software system failures, with the goal of understanding how we might better evolve, maintain and support software systems in production. We have qualitatively analyzed thirty…

Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions---like taxation, justice, and child protection---are now commonplace. How might designers support such human values? We…

Recent studies show that gender diversity in IT teams has a positive impact on the software development process. However, there is still a great gender inequality. The aim of our study was to examine how the working atmosphere depends on…

Our narrative literature review acknowledges that, although there is an increasing interest in recommender system fairness in general, the music domain has received relatively little attention in this regard. However, addressing fairness of…

Algorithmic fairness research has traditionally been linked to the disciplines of philosophy, ethics, and economics, where notions of fairness are prescriptive and seek objectivity. Increasingly, however, scholars are turning to the study…

Rankings of people and items has been highly used in selection-making, match-making, and recommendation algorithms that have been deployed on ranging of platforms from employment websites to searching tools. The ranking position of a…

In recent years, discussions about fairness in machine learning, AI ethics and algorithm audits have increased. Many entities have developed framework guidance to establish a baseline rubric for fairness and accountability. However, in…

Invisible labor is work that is either not fully visible or not appropriately compensated. In open source software (OSS) ecosystems, essential tasks that do not involve code (like content moderation) often become invisible to the detriment…

The intersection of ageism and sexism can create a hostile environment for veteran software developers belonging to marginalized genders. In this study, we conducted 14 interviews to examine the experiences of people at this intersection,…

Background: While bug bounty programs are not new in software development, an increasing number of companies, as well as open source projects, rely on external parties to perform the security assessment of their software for reward.…

In the current landscape of ever-increasing levels of digitalization, we are facing major challenges pertaining to scalability. Recommender systems have become irreplaceable both for helping users navigate the increasing amounts of data…

Background: Despite a long history, numerous laws and regulations, ethics remains an unnatural topic for many software engineering researchers. Poor research ethics may lead to mistrust of research results, lost funding and retraction of…

For more than 30 years, it has been claimed that a way to improve software developers' productivity and software quality is to focus on people and to provide incentives to make developers satisfied and happy. This claim has rarely been…

AI systems are increasingly used in high-stakes domains such as credit rating, where fairness concerns are critical. Existing fairness assessments are typically conducted by AI experts or regulators using predefined protected attributes and…

Given programming languages can provide different types and levels of security support, it is critically important to consider security aspects while selecting programming languages for developing software systems. Inadequate consideration…

We explore the fairness issue that arises in recommender systems. Biased data due to inherent stereotypes of particular groups (e.g., male students' average rating on mathematics is often higher than that on humanities, and vice versa for…