Abstract Many modern software applications present numerous ethical concerns due to conflicts between users’ values and companies’ priorities. Intersectional communities, those with multiple marginalized identities, are disproportionately affected by these ethical issues, leading to legal, financial, and reputational consequences for software companies, as well as real-world harm for intersectional users. Historically, the voices of intersectional communities have been systematically marginalized and excluded from contributing their unique perspectives to software design, perpetuating software-related ethical concerns. This work aims to fill the gap in research on intersectional users’ software-related perspectives and provide software practitioners with a methodology for analyzing intersectional voices in software ethics discourse. We collected 36,777 posts from over 700 intersectional subreddits discussing software applications and utilized large language models to identify ethical concerns in these posts. We then applied regression models with counterfactual analysis to examine how intersectional identity dimensions shape the amplification or suppression of ethical concern expression across software genres, and conducted a time-series analysis to examine how concern expression varies over time in relation to real-world events. As a case study in the social media domain, we further demonstrate how identified ethical concerns can be prioritized to surface issues warranting timely developer attention, validated against survey-derived ground truth. Together, these analyses form the basis of a nascent feedback-driven framework for assessing whether software systems are meeting the needs of intersectional users.

Abstract Software is a crucial component of modern everyday life. However, ethical issues in software, such as privacy issues, censorship and behavior manipulation are prevalent in software applications. To develop ethical software, it is essential to understand the ethical concerns of its end-users. To provide these insights, we conducted a survey among 725 participants and analyze which ethical concerns they have regarding software and why, as well as the actions they are willing to take when confronted with ethical issues in software. The results indicate that privacy, scam and misinformation are important ethical concerns for the wide majority, with scam prompting the strongest reaction in all unethical software scenarios. Reasons for these concerns included—but are not limited to—the exposure of personal sensitive information, perceived societal harm and fear of material loss. The most common reactions to ethical issues in software are to stop using the software and encourage friends and family to do the same. We also find that perceived importance and reactions to unethical scenarios in software products varies significantly by gender, education, and continent.

Software is an integral part of modern everyday life. However, recent ethical issues in software, such as privacy issues, censorship and behaviour manipulation are prevalent in software applications. To develop ethical software, it is crucial to understand the ethical concerns of its end-users. To provide these insights, we conducted a survey among 511 participants and analyze which ethical concerns they have regarding software and why. The results indicate that privacy and scam are important ethical concerns for the majority. Reasons included—but not limited to—are the exposure of personal sensitive information and fear of material loss. Our results show that end-users’ gender and education level significantly influence the perceived importance of several ethical concerns. Moreover, whether someone is from the Global North or Global South significantly influences how one would respond when faced with an ethical issue.

Historically, cisgender men have maintained systemic social, cultural, and political privilege over other genders. Online discrimination serves as a mechanism for reinforcing this dominance. Content moderation plays a crucial role in shaping online experiences, yet the ways it may perpetuate or mitigate discrimination remain underexplored. This study examines how content moderation and discussions of discrimination vary across gendered online communities, with a focus on identifying differential impacts by gender group. We analyzed 124 subreddits spanning three gender groups—women, men, and gender minorities (GM). The analysis included manual annotation of 1,535 posts and machine learning classification of an additional 6,613 posts to assess the prevalence of user discussions regarding online discrimination and content moderation. Women were most likely to report top-down moderation issues, such as bans and content removal, while GM users engaged more frequently with general moderation concerns. Time series analysis revealed that complaints about content moderation have increased over time, with the steepest rise among women users. These patterns demonstrate that moderation policies and enforcement impact gender groups differently. Our findings highlight the need for improvements in software engineering and user experience design for content moderation tools. Enhancing transparency, promoting equity, and enabling more user-driven moderation experiences are critical steps toward protecting marginalized groups against discrimination online.

Ethical concerns about software applications, e.g., worries about privacy breaches, user manipulation, and discrimination, have gained prominence recently. Research shows that users voice these concerns in app reviews and that they can be detected using machine learning and deep learning techniques. These techniques usually operate as black-boxes, making it difficult to understand the context of users' ethical concerns. We address this issue by presenting a transparent approach that uses pattern mining and graph theory to yield additional context to the ethical concern classifications made by machine learning algorithms. We compare a simple frequent pattern mining and a high-utility mining algorithm and assess the resulting rules through commonly used metrics. Finally, we visualize and interpret preliminary results in an interactive graph. We mined 3,101 reviews of ten popular apps mentioning diverse ethical concerns and present the results for two apps in detail. Our results show that pattern mining algorithms and graph visualizations are promising directions for detecting contextual information of ethical concerns about software. This work is a step toward ensuring that ethical concerns are methodically thought through and integrated into the software development life cycle.

Abstract Gender diversity and equity are known problems in the software industry. However, relatively few studies has examined the everyday work experiences and barriers that software professionals in technical roles encounter through a gender perspective. In this work, we investigate micro-inequities (e.g., interruptions, lack of eye contact, being assigned menial tasks in a project) and barriers experienced by software professionals working in technical roles with a gender perspective. We also analyzed age as a confounding factor. In our study, we surveyed 359 software professionals (50:50, women:men ratio) from globally distributed locations. Our results show that women and respondents in certain age groups encounter micro-inequities significantly more than men and other age groups. Further, women experience and witness sexism and harassment in the workplace in significantly higher numbers. We also found that women report having significantly less support and authority to make necessary decisions in their work, are less satisfied with their pay, and feel less valued and recognized in their teams. Finally, we found that the main barriers reported by women are related to team dynamics and gender biases, while men report most on technical and project related issues. Our results can serve to create awareness in the community about the large disparity and help practitioners revise their training programs and internal policies.

Abstract This work analyzes ethical concerns found in users’ app store reviews. We performed this study because ethical concerns in mobile applications (apps) are widespread, pose severe threats to end users and society, and lack systematic analysis and methods for detection and classification. In addition, app store reviews allow practitioners to collect users’ perspectives, crucial for identifying software flaws, from a geographically distributed and large-scale audience. For our analysis, we collected five million user reviews, developed a set of ethical concerns representative of user preferences, and manually labeled a sample of these reviews. We found that (1) users highly report ethical concerns about censorship, identity theft, and safety (2) user reviews with ethical concerns are longer, more popular, and lowly rated, and (3) there is high automation potential for the classification and filtering of these reviews. Our results highlight the relevance of using app store reviews for the systematic consideration of ethical concerns during software evolution.

Background: Wrongdoings occurring within or in relation to software can have big implications on individuals, groups of people, or society as a whole. Whistleblowing is considered an effective tool to reveal and stop wrongdoing but is still a controversial topic that has been researched sparsely in the software industry. Aim: In this study we address this gap and research the current environment for whistleblowing (reporting wrongdoing) in the software industry. Method: We surveyed 147 software practitioners about their views on whistleblowing, the current means they have to report software-related wrongdoing, and the enabling and obstructing factors to whistleblow. Results: Our study shows that software practitioners have a positive view towards whistleblowing. However, in practice whistleblowing is obstructed by the difficulty of proving the actual harm and fear of retaliation. Practitioners with more years of experience report more comfort using readily established mechanisms and procedures in their organization, are more willing to speak up and have more confidence that their report will lead to action than their less experienced peers. These differences are statistically significant. Conclusion: Through our results we conclude that the software industry needs to improve the environment for whistleblowers by providing more external reporting mechanisms, anonymity, and confidentiality, as well as support practitioners with less years of experience.

From airports to banks, healthcare, space crafts, and even amazon services, technology impacts almost every aspect of today’s life. If wrongdoings occur within or in relation to technology, they can have big implications on individuals, groups of people, or society as a whole. Whistleblowers are insiders who expose such wrongdoings— eventually stopping misconducts, such as fraud, endangerment to public health and safety, or damage to the environment. Twitter is a microblogging service that allows millions of users to share their views with people distributed all over the world on a daily basis. Tweets have the potential to contain useful information about whistleblowing in tech, from the general public and whistleblowers. However, until now this point has not been researched.To fill this gap, we conducted an exploratory study on technology-related whistleblowing tweets by manually analysing tweets, utilising descriptive statistics, and machine learning techniques. We mined 7,400 tweets from whistleblowers themselves, as well as news and opinions about certain whistleblowers and whistleblowing cases. Although our results show that only 30% of the tweets in our sample dataset (obtained through specific search terms) contained relevant information about whistleblowing in technology, our analysis shows that tweets provide valuable information for both researchers and companies to understand the public opinion regarding whistleblowing cases. Furthermore, we found that machine learning techniques are promising means for extracting information about whistleblowing in tech from the vast stream of tweets.

Background: Q&A sites allow to study how users reference and request support on technical debt. To date only few studies, focusing on narrow aspects, investigate technical debt on Stack Overflow.

Robots are becoming more and more commonplace in many industry settings. This successful adoption can be partly attributed to (1) their increasingly affordable cost and (2) the possibility of developing intelligent, software-driven robots. Unfortunately, robotics software consumes significant amounts of energy. Moreover, robots are often battery-driven, meaning that even a small energy improvement can help reduce its energy footprint and increase its autonomy and user experience.In this paper, we study the Robot Operating System (ROS) ecosystem, the de-facto standard for developing and prototyping robotics software. We analyze 527 energy-related data points (including commits, pull-requests and issues on ROS-related repositories, ROS-related questions on StackOverflow, ROS Discourse, ROS Answers and the official ROS Wiki).Our results include a quantification of the interest of roboticists on software energy efficiency, 10 recurrent causes and 14 solutions of energy-related issues, and their implied trade-offs with respect to other quality attributes. Those contributions support roboticists and researchers towards having energy-efficient software in future robotics projects.

User feedback is indispensable in software evolution. Previous work has proposed ways for automatically extracting requirements, bug reports and other valuable information from feedback. However, little is actually known about how user feedback— especially the one available through newer channels, such as social media—is incorporated in development processes. To date, only a few case studies discuss the matter and the results are not always consistent. We carried out a mixed methods study to understand the current state of practice of harnessing user feedback in software development. Qualitatively, we performed interviews with 18 software practitioners to get a deeper understanding of the role of user feedback in software evolution. Quantitatively, we surveyed 101 software practitioners to cross-validate the interview findings and improve the generalizability of the results. We found that feedback is captured to (1) identify bugs, features and usability issues, (2) get a better understanding of the user, and (3) prioritize requirements. Our results indicate that analyzing feedback is time-consuming and has a number of challenges. Among them, feedback is typically analyzed manually and is spread over a wide range of channels and company departments. Our findings stress the current importance for cross-department cooperation and call for the exploration of tools that can centralize user feedback.

Users submit feedback about the software they use through application distributions platforms, i.e., app stores, and social media. Previous research has found that this type of feedback contains valuable information for software evolution, such as bug reports, or feature requests. However, popular applications receive thousands of feedback entities per day, making their manual analysis unrealistic. In this work, we present an approach to automatically identify similar user feedback across different languages and platforms. At the core of the approach is a word aligner that aligns words based on their semantic similarity and the similarity of their local semantic contexts. Additionally, we make use of machine translation, sentiment analysis, and text classification, to extract the sentiment polarity and content nature of user feedback written in different languages. We use the results of these components to compute a similarity score between user feedback pairs. We evaluated our approach on user feedback entities written in four different languages, and retrieved from five different mobile applications obtained from four different app stores and social networking sites. The obtained results are encouraging. Compared to human assessment, the overall performance for monolingual user feedback pairs yielded a strong correlation of 0.79. For the crosslingual feedback pairs the correlation was also strong, with a value of 0.78.

Through app stores, users can submit feedback in the form of user reviews. Previous work has found that these reviews contain useful information such as user requirements and bug reports; and has presented approaches for automatically extracting this information. However, the differences in the feedback submitted by female and male users and its consequences with respect to algorithm bias have not been studied so far. In this paper, we take a step in this direction and report on an exploratory study that investigates 919 reviews from eight countries written by users with usernames identified by manual analysis as female or male. We contribute initial evidence of a possible imbalance in the number of female and males users writing app reviews. Additionally, while this disproportion exists, the analyzed feedback between female and male users is similar in terms of the expressed sentiment, content, rating, timing and length. These variables are commonly used when prioritizing user feedback for their later use during software evolution. Although we need a larger sample size to generalize our results, the similarities we report hint that gender bias is not a threat for feedback processing algorithms which exclusively take into account the characteristics studied in this work.

Developers make various decisions during software development. The rationale behind these decisions is of great importance during software evolution of long living software systems. However, current practices for documenting rationale often fall short and rationale remains hidden in the heads of developers or embedded in development artifacts. Further challenges are faced for capturing rationale in OSS projects; in which developers are geographically distributed and rely mostly on written communication channels to support and coordinate their activities. In this paper, we present an empirical study to understand how OSS developers discuss rationale in IRC channels and explore the possibility of automatic extraction of rationale elements by analyzing IRC messages of development teams. To achieve this, we manually analyzed 7,500 messages of three large OSS projects and identified all fine-grained elements of rationale. We evaluated various machine learning algorithms for automatically detecting and classifying rationale in IRC messages. Our results show that 1) rationale is discussed on average in 25% of IRC messages, 2) code committers contributed on average 54% of the discussed rationale, and 3) machine learning algorithms can detect rationale with 0.76 precision and 0.79 recall, and classify messages into finer-grained rationale elements with an average of 0.45 precision and 0.43 recall.

Developers' chat messages are a rich source of rationale behind development decisions. Rationale comprises valuable knowledge during software evolution for understanding and maintaining the software system. However, developers resist explicit methods for rationale capturing in practice, due to their intrusiveness and cognitive overhead. Aim: Our primary goal is to help developers capture rationale in chat messages with low effort. Further, we seek to encourage the collaborative capturing of rationale in development teams. Method: In this paper, we present REACT, a lightweight approach for annotating chat messages that contain rationale. To evaluate the feasibility of REACT, we conducted two studies. In the first study, we evaluated the approach with eleven development teams during a short-term design task. In the second study, we evaluated the approach with one development team over a duration of two months. In addition, we distributed a questionnaire to both studies' participants. Results: Our results show that REACT is easily learned and used by developers. Furthermore, it encourages the collaborative capturing of rationale. Remarkably, the majority of participants do not perceive privacy as a barrier when capturing rationale from their informal communication. Conclusions: REACT is a first step towards enhancing rationale capturing in developers' chat messages.

Chat messages of development teams play an increasinglysignificant role in software development, having replacedemails in some cases. Chat messages contain informationabout discussed issues, considered alternatives and argumentationleading to the decisions made during software development. These elements, defined as rationale, are invaluable duringsoftware evolution for documenting and reusing developmentknowledge. Rationale is also essential for coping with changesand for effective maintenance of the software system. However, exploiting the rationale hidden in the chat messages is challengingdue to the high volume of unstructured messages covering a widerange of topics. This work presents the results of an exploratorystudy examining the frequency of rationale in chat messages, the completeness of the available rationale and the potential ofautomatic techniques for rationale extraction. For this purpose, we apply content analysis and machine learning techniques onmore than 8,700 chat messages from three software developmentprojects. Our results show that chat messages are a rich source ofrationale and that machine learning is a promising technique fordetecting rationale and identifying different rationale elements.

Twitter is one of the most popular social networks. Previous research found that users employ Twitter to communicate about software applications via short messages, commonly referred to as tweets, and that these tweets can be useful for requirements engineering and software evolution. However, due to their large number---in the range of thousands per day for popular applications---a manual analysis is unfeasible.In this work we present ALERTme, an approach to automatically classify, group and rank tweets about software applications. We apply machine learning techniques for automatically classifying tweets requesting improvements, topic modeling for grouping semantically related tweets and a weighted function for ranking tweets according to specific attributes, such as content category, sentiment and number of retweets. We ran our approach on 68,108 collected tweets from three software applications and compared its results against software practitioners' judgement. Our results show that ALERTme is an effective approach for filtering, summarizing and ranking tweets about software applications. ALERTme enables the exploitation of Twitter as a feedback channel for information relevant to software evolution, including end-user requirements.

Users of the Twitter microblogging platform share a vast amount of information about various topics through short messages on a daily basis. Some of these so called tweets include information that is relevant for software companies and could, for example, help requirements engineers to identify user needs. Therefore, tweets have the potential to aid in the continuous evolution of software applications. Despite the existence of such relevant tweets, little is known about their number and content. In this paper we report on the results of an exploratory study in which we analyzed the usage characteristics, content and automatic classification potential of tweets about software applications by using descriptive statistics, content analysis and machine learning techniques. Although the manual search of relevant information within the vast stream of tweets can be compared to looking for a needle in a haystack, our analysis shows that tweets provide a valuable input for software companies. Furthermore, our results demonstrate that machine learning techniques have the capacity to identify and harvest relevant information automatically.

Google Play, Apple App Store and Windows Phone Store are well known distribution platforms where users can download mobile apps, rate them and write review comments about the apps they are using. Previous research studies demonstrated that these reviews contain important information to help developers improve their apps. However, analyzing reviews is challenging due to the large amount of reviews posted every day, the unstructured nature of reviews and its varying quality.

Context: Users can have conflicting opinions and different experiences when using software and user reviews serve as a channel in which users can document their opinions and experiences. To develop and evolve software that is usable and relevant for a diverse group of users, different opinions and experiences need to be taken into account. Goal: In this paper we present DIVERSE, a feature and sentiment centric retrieval approach which automatically provides developers with a diverse sample of user reviews that is representative of the different opinions and experiences mentioned in the whole set of reviews. Results: We evaluated the diversity retrieval performance of our approach on reviews from seven apps from two different app stores. We compared the reviews retrieved by DIVERSE with a feature-based retrieval approach and found that on average DIVERSE outperforms the baseline approach. Additionally, a controlled experiment revealed that DIVERSE can help develop- ers save time when analyzing user reviews and was considered useful for detecting conflicting opinions and software evolution. Conclusions: DIVERSE can therefore help developers collect a comprehensive set of reviews and aid in the detection of conflicting opinions.

App Stores, such as Google Play or the Apple Store, allow users to provide feedback on apps by posting review comments and giving star ratings. These platforms constitute a useful electronic mean in which application developers and users can productively exchange information about apps. Previous research showed that users feedback contains usage scenarios, bug reports and feature requests, that can help app developers to accomplish software maintenance and evolution tasks. However, in the case of the most popular apps, the large amount of received feedback, its unstructured nature and varying quality can make the identification of useful user feedback a very challenging task. In this paper we present a taxonomy to classify app reviews into categories relevant to software maintenance and evolution, as well as an approach that merges three techniques: (1) Natural Language Processing, (2) Text Analysis and (3) Sentiment Analysis to automatically classify app reviews into the proposed categories. We show that the combined use of these techniques allows to achieve better results (a precision of 75% and a recall of 74%) than results obtained using each technique individually (precision of 70% and a recall of 67%).

App marketplaces are distribution platforms for mobile applications that serve as a communication channel between users and developers. These platforms allow users to write reviews about downloaded apps. Recent studies found that such reviews include information that is useful for software evolution. However, the manual analysis of a large amount of user reviews is a tedious and time consuming task. In this work we propose a taxonomy for classifying app reviews into categories relevant for software evolution. Additionally, we describe an experiment that investigates the performance of individual machine learning algorithms and its ensembles for automatically classifying the app reviews. We evaluated the performance of the machine learning techniques on 4550 reviews that were systematically labeled using content analysis methods. Overall, the ensembles had a better performance than the individual classifiers, with an average precision of 0.74 and 0.59 recall.

Emotions have a high impact in productivity, task quality, creativity, group rapport and job satisfaction. In this work we use lexical sentiment analysis to study emotions expressed in commit comments of different open source projects and analyze their relationship with different factors such as used programming language, time and day of the week in which the commit was made, team distribution and project approval. Our results show that projects developed in Java tend to have more negative commit comments, and that projects that have more distributed teams tend to have a higher positive polarity in their emotional content. Additionally, we found that commit comments written on Mondays tend to a more negative emotion. While our results need to be confirmed by a more representative sample they are an initial step into the study of emotions and related factors in open source projects.

App stores allow users to submit feedback for downloaded apps in form of star ratings and text reviews. Recent studies analyzed this feedback and found that it includes information useful for app developers, such as user requirements, ideas for improvements, user sentiments about specific features, and descriptions of experiences with these features. However, for many apps, the amount of reviews is too large to be processed manually and their quality varies largely. The star ratings are given to the whole app and developers do not have a mean to analyze the feedback for the single features. In this paper we propose an automated approach that helps developers filter, aggregate, and analyze user reviews. We use natural language processing techniques to identify fine-grained app features in the reviews. We then extract the user sentiments about the identified features and give them a general score across all reviews. Finally, we use topic modeling techniques to group fine-grained features into more meaningful high-level features. We evaluated our approach with 7 apps from the Apple App Store and Google Play Store and compared its results with a manually, peer-conducted analysis of the reviews. On average, our approach has a precision of 0.59 and a recall of 0.51. The extracted features were coherent and relevant to requirements evolution tasks. Our approach can help app developers to systematically analyze user opinions about single features and filter irrelevant reviews.

App users can submit feedback about downloaded apps by writing review comments and giving star ratings directly in the distribution platforms. Previous research has shown that this type of feedback contains important information for software evolution. However, in the case of the most popular apps, the amount of received feedback and its unstructured nature can produce difficulties in its analysis. We present an interactive user feedback visualization which displays app reviews from four different points of view: general, review based, feature based and topic-feature based. We conducted a study which visualized 2009 reviews from the Dropbox app available in the App Store. Participants considered the approach useful for software evolution tasks as they found it could aid developers and analysts get an overview of the most and least popular app features, and to prioritize their work. While using different strategies to find relevant information during the study, most participants came to the same conclusions regarding the user reviews and assigned tasks.

Emotions play an important role in determining work results and how team members collaborate within a project. When working in large, distributed teams, members can lose awareness of the emotional state of the project. We propose an approach to improve emotional awareness in software development teams by means of quantitative emotion summaries. Our approach automatically extracts and summarizes emotions expressed in collaboration artifacts by combining probabilistic topic modeling with lexical sentiment analysis techniques. We applied the approach to 1000 collaboration artifacts produced by three development teams in a three month period. Interviews with the teams' project leaders suggest that the proposed emotion summaries have a good correlation with the emotional state of the project, and could be useful for improving emotional awareness. However, the interviews also indicate that the current state of the summaries is not detailed enough and further improvements are needed.

Developers and managers need to be aware of the emotional climate of the projects they are involved to take corrective actions when necessary and to have a better understanding of the social factors affecting the project. With the growing trend of distributed teams and textual communication this type of awareness is more difficult to obtain and maintain. We propose to improve emotional climate awareness in software development projects by means of a visualization prototype which includes general and detailed views of the topics and emotions expressed in software project collaboration artifacts. We performed an initial case study in which the mailing list content of a software project was visualized. The study suggests that the length, frequency and emotion diversity of the exchanged content varies according to the project phase. However, a more extensive evaluation needs to be made.