Requirements engineering is a communication-intensive activity and thus it suffers much from language difficulties in global software projects. Remote requirements meetings can benefit from machine translation as this technology is today available in the form of cross-language chat services. In this paper, we present the design of a controlled experiment to investigate the effects of automatic machine translation services in requirements meetings. Experiment participants, using either Italian or Portuguese as native language, are asked to interact with a communication tool from a distance in order to prioritize and estimate requirements. First results show that real-time machine translation is not disruptive of the conversation flow and is accepted with favor by participants. However, concrete effects are expected to emerge when language barriers are critical.

Today distributed development depend on an ever-growing plethora of tools that provide a continual stream of updates and place developers into a situation of channel overload and information fragmentation. In this paper, we present our initial work on the definition of a model, named hub-and-spoke, for a loosely-coupled integration of development tools that can help developers cope with these issues, while also increasing their overall situational awareness.

Estimating and planning are critical to the success of any software project, also in the case of distributed agile development. Previous research has acknowledged that conventional agile methods need to be adjusted when applied in distributed contexts. However, we argue that also new tools are needed for enabling effective distributed agile practices. Here, we present eConference3P, a tool for supporting distributed agile teams who applies the planning poker technique to perform collaborative user story estimation. The planning poker technique builds on the combination of multiple expert opinions, represented using the visual metaphor of poker cards, which results in quick but reliable estimates.

We present the preliminary results of an ongoing research aimed at investigating the role of social media in the process of trust building, with particular attention to the case of small-medium enterprises (SME). Our findings show that social media contribute to increase the affective trust more than traditional websites. This result suggests that social media have the potential to enhance the business of SMEs other than large companies, by fostering the affective commitment of customers.

Technical Q\&A sites have become essential for software engineers as they constantly seek help from other experts to solve their work problems. Despite their success, many questions remain unresolved, sometimes because the asker does not acknowledge any helpful answer. In these cases, an information seeker can only browse all the answers within a question thread to assess their quality as potential solutions. We approach this time-consuming problem as a binary-classification task where a best-answer prediction model is built to identify the accepted answer among those within a resolved question thread, and the candidate solutions to those questions that have received answers but are still unresolved. In this paper, we report on a study aimed at assessing 26 best-answer prediction models in two steps. First, we study how models perform when predicting best answers in Stack Overflow, the most popular Q\&A site for software engineers. Then, we assess performance in a cross-platform setting where the prediction models are trained on Stack Overflow and tested on other technical Q\&A sites. Our findings show that the choice of the classifier and automatied parameter tuning have a large impact on the prediction of the best answer. We also demonstrate that our approach to the best-answer prediction problem is generalizable across technical Q\&A sites. Finally, we provide practical recommendations to Q\&A platform designers to curate and preserve the crowdsourced knowledge shared through these sites.

Context: Real-time speech translation technology is today available but still lacks a complete understanding of how such technology may affect communication in global software projects. Goal: To investigate the adoption of combining speech recognition and machine translation in order to overcome language barriers among stakeholders who are remotely negotiating software requirements. Method: We performed an empirical simulation-based study including: Google Web Speech API and Google Translate service, two groups of four subjects, speaking Italian and Brazilian Portuguese, and a test set of 60 technical and non-technical utterances. Results: Our findings revealed that, overall: (i) a satisfactory accuracy in terms of speech recognition was achieved, although significantly affected by speaker and utterance differences; (ii) adequate translations tend to follow accurate transcripts, meaning that speech recognition is the most critical part for speech translation technology. Conclusions: Results provide a positive albeit initial evidence towards the possibility to use speech translation technologies to help globally distributed team members to communicate in their native languages.

Communication in global software development is hindered by language differences in countries with a lack of English speaking professionals. Machine translation is a technology that uses software to translate from one natural language to another. The progress of machine translation systems has been steady in the last decade. As for now, machine translation technology is particularly appealing because it might be used, in the form of cross-language chat services, in countries that are entering into global software projects. However, despite the recent progress of the technology, we still lack a thorough understanding of how real-time machine translation affects communication. In this paper, we present a set of empirical studies with the goal of assessing to what extent real-time machine translation can be used in distributed, multilingual requirements meetings instead of English. Results suggest that, despite far from 100% accurate, real-time machine translation is not disruptive of the conversation flow and, therefore, is accepted with favor by participants. However, stronger effects can be expected to emerge when language barriers are more critical. Our findings add to the evidence about the recent advances of machine translation technology and provide some guidance to global software engineering practitioners in regarding the losses and gains of using English as a lingua franca in multilingual group communication, as in the case of computer-mediated requirements meetings.

Opportunities for global software development are limited in those countries with a lack of English-speaking professionals. Machine translation technology is today available in the form of cross-language web services and can be embedded into multiuser and multilingual chats without disrupting the conversation flow. However, we still lack a thorough understanding of how real-time machine translation may affect communication in global software teams. In this paper, we present the replication of a controlled experiment that assesses the effect of real-time machine translation on multilingual teams while engaged in distributed requirements meetings. In particular, in this replication we specifically evaluate whether non-English speaking groups benefit from communicating in their own native languages when their English is not fluid enough for a fast-paced conversation.

Adequate tool support is paramount to enable distributed teamwork, and thus global software teams usually rely on a Collaborative Development Environment (CDE) to cope with geographical distance. The most recent and full-featured CDEs typically provide presence and workspace awareness in one place, but lack any support to social awareness for reducing the sociocultural distance. We argue that disseminating social awareness information within a CDE can both speed up the establishment of a cross-organizational shared context and help developers who have little or no chances to meet and, then, develop trust-based inter-personal connections. For this reason, we propose to extend a commercial CDE in order to provide members of global software teams with information collected from corporate microblogging and professional social networks.

Social awareness, that is information that a person maintains about others in a social or conversational context, can contribute to counteract the lack of teamness in global software development and strengthen trust among remote developers. We hypothesize that information shared on social media can work for distributed software teams as a surrogate of the social awareness gained during informal face to face chats. As a preliminary step we have developed a tool that extends a collaborative development environment by aggregating content from social networks and microblogs into the developer’s workspace.

Trust is paramount in distributed software development to prevent geographically distributed sites to feel and act like distinct, distant teams. Nevertheless, how to build trust among developers with few or no chances to meet is an open issue. To overcome such a challenge we hypothesize that increased social awareness may foster trust building in global software teams. Here, we first present SocialCDE, a tool that aims at augmenting Application Lifecycle Management (ALM) platforms with social awareness to facilitate the establishment of interpersonal connections by disclosing developers’ personal interests and contextual information. Then, we present two different empirical studies, specifically designed to test our hypothesis.

In global software projects work takes place over long distances, meaning that communication will often involve distant cultures with different languages and communication styles that, in turn, exacerbate communication problems. However, being aware of cultural distance is not sufficient to overcome many of the barriers that language differences bring in the way of global project success. In this paper, we investigate the adoption of machine translation (MT) services in synchronous text-based chat in order to overcome any language barrier existing among groups of stakeholders who are remotely negotiating software requirements. We report our findings from a simulated study that compares the efficiency and the effectiveness of two MT services, Google Translate and apertium-service, in translating the messages exchanged during four distributed requirements engineering workshops. The results show that (a) Google Translate produces significantly more adequate translations than Apertium from English to Italian; (b) both services can be used in text-based chat without disrupting real-time interaction.

Trust is paramount in distributed software development to prevent geographically distributed sites to feel distant and act like distinct teams with own conflicting goals. Nevertheless, how to build trust among developers with few or no chances to meet is an open issue. To overcome such a challenge, we hypothesize that increased social awareness may foster trust building in global software teams. In this paper, we present two different empirical studies, specifically designed to test our hypotheses.

Nowadays work is becoming predominantly distributed, bringing significant challenges to effective communication of geographically dispersed groups. In fact, multisite work presents considerable loss of opportunities for rich interaction and a very substantial reduction in frequency of both formal and informal communication between coworkers. While communicating face-to-face (F2F) by speech is easy for individuals, conducting a long-running, productive conversation through the digital medium is difficult, especially as the group size increases. The difficulty of computer-mediated communication (CMC) and collaboration stands in stark contrast to our natural ability to easily communicate and collaborate with one another in the physical world. As such, there is a need to further our understanding of the effectiveness of the many available synchronous and asynchronous communication media (e.g., e-mail, videoconferencing, or specialized collaboration tools) to support activities of distributed teams. However, not only media properties (e.g., synchronicity) affect the performance of groups collaborating from a distance but also the characteristics of groups (e.g., size, history) and tasks (e.g., idea generation, decision making) play a key role. In this chapter, we first present a survey on the group-, task-, and media-related theories that are relevant for the selection of the most appropriate synchronous communication media to better support distributed ad hoc groups, that is, short-term groups with neither a history of previous collaborations nor expectation of future ones. Then, we consistently combine all the reviewed theories to create two general models that, respectively, can help researchers to manage the context of experiments on remote group collaboration, and distributed groups themselves to evaluate, compare, and select the most appropriate fits between the task at a hand and the media available.

Requirements engineering is one of the most communication-intensive activities in software development, greatly affected by project stakeholder geographical distribution. Despite advances in collaboration technologies, global software teams continue to experience significant challenges in the elicitation and negotiation of requirements. Deciding which communication technologies to deploy to achieve effective communication in distributed requirements engineering activities is not a trivial task. Is face-to-face or text-based communication more appropriate for requirements elicitations and negotiations? In teams that do not have access to face-to-face communication, is text-based communication more useful in requirements elicitations than in requirements negotiations? Here, we report an empirical study that analyzes the effectiveness of synchronous computer-mediated communication in requirements elicitations and negotiations. Our investigation is guided by a theoretical framework that we developed from theories of computer-mediated communication, common ground, and media selection for group tasks; a framework that considers the effectiveness of a communication medium in relation to the information richness needs of requirements elicitation and negotiation tasks. Our findings bring forward empirical evidence about the perceived as well as objective fit between synchronous communication technology and requirements tasks. First, face-to-face is not always the most preferred medium for requirements tasks, and we reveal a number of conditions in which, in contrast to common belief, text-based communication is preferred for requirements communication. Second, we find that in evaluating outcomes of requirements elicitations and negotiations objectively, group performance is not affected by the communication medium. Third, when groups interact only via text-based communication, common ground in requirements negotiations takes longer to achieve than in requirements elicitations, indicating that distributed requirements elicitation is the task where computer-mediated communication tools have most opportunity for successful application.

Insufficient team collaboration often challenges global software engineering projects. Group awareness can improve teams’ trust, relationships, and efficiency. This article surveys the key technologies and tools that support group awareness and collaboration.

Trust is a concept that has been widely studied in e-commerce since it represents a key issue in building successful customer-supplier relationships. In this sense, social software represents a powerful channel for establishing a direct communication with customers. As a consequence, companies are now investing in social media for building their social digital brand and strengthening relationships with their customers. In this paper we investigate the role of social media in the process of trust building, with particular attention to the case of small companies. Our findings show that social media contribute to build affective trust more than traditional websites, by fostering the affective commitment of customers.

Trust is a concept that has been widely studied in e-commerce since it represents a key issue in building successful customer-supplier relationships. In this sense, social software represents a powerful channel for establishing a direct communication with customers. As a consequence, companies are now investing in social media for building their social digital brand and strengthening relationships with their customers. In this paper, we present the preliminary results of an ongoing research aimed at investigating the role of social media in the process of trust building, with particular attention to the case of small-medium enterprises. Our findings show that social media contribute to build more affective trust than traditional websites, suggesting that social media have the potential to enhance the business of SMEs other than large companies, by fostering the affective commitment of customers.

Modern collaborative development environments have recently introduced tagging as a new feature in order to let developers annotate software artifacts with free keywords. Since tagging has the potential to have an impact on task management in software development processes, there is a need to understand how developers use tagging in projects supported by collaborative development environments and how developers' behavior differ from collaborative tagging in the Social Web. We have conducted an independent replication of an empirical study, which first investigated how tags are used in a large software project. In our replication, we have analyzed two further projects coordinated through two different collaborative development environments, Jazz and Trac. The findings from our replicated study extend the initial contribution of the original study by (1) showing evidence of differences in tag usage between the two collaborative development environments, and (2) providing a clear understanding that tags used in such environments significantly differs from those used in traditional collaborative tagging systems.

Recent research has shown that drivers of success in online question answering encompass presentation quality as well as temporal and social aspects. Yet, we argue that also the emotional style of a technical contribution influences its perceived quality. In this paper, we investigate how Stack Overflow users can increase the chance of getting their answer accepted. We focus on actionable factors that can be acted upon by users when writing an answer and making comments. We found evidence that factors related to information presentation, time and affect all have an impact on the success of answers.

Communication in global software projects usually occurs between native and non-native English speakers with the drawback of an unequal ability to fully understand and contribute to discussions. In this paper, we investigate the adoption of combining speech recognition and machine translation in order to overcome language barriers among stakeholders who are remotely negotiating software requirements. We report our findings from a simulated study where stakeholders communicate speaking three different languages with the help of the Google mobile speech translation service.

Context: Recently, more and more developer communities are abandoning their legacy support forums, moving onto Stack Overflow. The motivations are diverse, yet they typ-ically include achieving faster response time and larger vis-ibility through the access to a modern and very successful infrastructure. One downside of migration, however, is that the history and the crowdsourced knowledge hosted at pre-vious sites remain separated or even get lost if a community decides to abandon completely the legacy developer forum. Goal: Adding to the body of evidence of existing research on best-answer prediction, here we show that, from a techni-cal perspective, the content from existing developer forums might be automatically migrated to the Stack Overflow, al-though most of forums do not allow to mark a question as resolved, a distinctive feature of modern Q&A sites. Method: We trained a binary classifier with data from Stack Overflow and then tested it with data scraped from Do-cusign, a developer forum that has recently completed the move. Results: Our findings show that best answers can be pre-dicted with a good accuracy, only relying on shallow linguis-tic (text) features, such as answer length and the number of sentences, combined with other features like answer upvotes and age, which can be easily computed in near real-time. Conclusions: Results provide an initial yet positive ev-idence towards the automatic migration of crowdsourced knowledge from legacy forums to modern Q&A sites.

Large-scale distributed projects are typically the results of collective efforts performed by multiple developers, each one having a different personality. The study of developers’ personalities has the potential of explaining their’ behavior in various contexts. For example, the propensity to trust others, a critical factor to the success of global software engineering - has been found to influence positively the result of code reviews in distributed projects. In this paper, we perform a quantitative analysis of developers’ personality in open source software projects, intended as an extreme form of distributed projects in which no single organization controls the project. We mine ecosystem-level data from the code commits and email messages contributed by the developers working on the Apache Software Foundation (ASF) projects, as representative of large scale-distributed projects. We find that developers’ personality evolves over time as more conscientious, agreeable, and neurotic. Instead, personality traits do not vary with their role and extent of contribution to the projects. We also find evidence that more open and more agreeable developers are more likely to become project contributors.

Non-governmental organizations (NGOs) are often plagued by very limited human and financial resources. In this paper, we show how product line engineering (PLE) offers an opportunity to increase the sustainability of software projects that rely on the help of NGO volunteers. Building on the case of an Italian NGO that supports assistive technologies, we propose a PLE model that only depends on the branching capability of a free version control system.

Opportunities for global software development are limited in those countries with a lack of English-speaking professionals. Machine translation technology is today available in the form of cross-language web services and can be embedded into multiuser and multilingual chats without disrupting the conversation flow. However, we still lack a thorough understanding of how real-time machine translation may affect communication in global software teams. In this paper, we present a program of research related to real-time machine translation where we aim at investigating how MT technology could be used by software development teams located in countries where professionals are not proficient in one common language. We present the studies executed so far, including text-based and voice-based machine translation, as well as the next steps planned for this research.

Revision control is a vital component in collaborative development of artifacts such as software code and multimedia. While revision control has been widely deployed for text files, very few attempts to control the versioning of binary files can be found in the literature. This can be inconvenient for graphics applications that use a significant amount of binary data, such as images, videos, meshes, and animations. Existing strategies such as storing whole files for individual revisions or simple binary deltas, respectively consume significant storage and obscure semantic information. To overcome these limitations, in this paper we present a revision control system for digital images that stores revisions in form of graphs. Besides, being integrated with Git, our revision control system also facilitates artistic creation processes in common image editing and digital painting workflows. A preliminary user study demonstrates the usability of the proposed system.

The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within social software engineering tools. However, off-the-shelf sentiment analysis tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports. Here, we present Senti4SD, a classifier specifically trained to support sentiment analysis in developers' communication channels. Senti4SD is trained and validated using a gold standard of Stack Overflow questions, answers, and comments manually annotated for sentiment polarity. It exploits a suite of both lexicon- and keyword-based features, as well as semantic features based on word embedding. With respect to a mainstream off-the-shelf tool, which we use as a baseline, Senti4SD reduces the misclassifications of neutral and positive posts as emotionally negative. To encourage replications, we release a lab package including the classifier, the word embedding space, and the gold standard with annotation guidelines.

In this paper, we present the proposal for a partial replication of a controlled experiment to further assess how knowing personal and expertise information about other team members may enhance initial trust building. Other than increasing confidence into the findings of the original study, we also aim at evaluating whether the provision of personal social media information, can lead to even higher level of trust in virtual teams.

We present SocialCDE, a tool that aims at augmenting Application Lifecycle Management (ALM) platforms with social awareness to facilitate the establishment of interpersonal connections and increase the likelihood of successful interactions by disclosing developers’ personal interests and contextual information.

Nowadays, people increasingly seek information and ask for help on Question and Answer (Q&A) sites. The enormous success of Stack Exchange , a constantly growing network of Q&A sites, attests this increasing trend. The success of Q&A mainly depends on the will of their members to provide good quality answers to others’ questions. We investigate the success factors of Q&A that is those factors that foster effective knowledge creation and sharing. In particular, we focus those factors that can be acted upon by contributors when writing a question.

A recent research trend has emerged to study the role of affect in in the social programmer ecosystem, by applying sentiment analysis to the content available in sites such as GitHub and Stack Overflow. In this paper, we aim at assessing the suitability of a state-of-the-art sentiment analysis tool, already applied in social computing, for detecting affective expressions in Stack Overflow. We also aim at verifying the construct validity of choosing sentiment polarity and strength as an appropriate way to operationalize affective states in empirical studies on Stack Overflow. Finally, we underline the need to overcome the limitations induced by domain-dependent use of lexicon that may produce unreliable results.

Trust represents a key issue in building successful customer-supplier relationships. In this sense, social software represents a powerful means for fostering trust by establishing a direct, more personal communication channel with customers. Therefore, companies are now investing in so-cial media for building their social digital brand and strengthening relationships with their cus-tomers. In this paper, we presented two experiments by means of which we investigated the role of traditional websites and social media in trust building along the cognitive and affective di-mensions. We hypothesize that traditional websites (content-oriented) and social media (interac-tion-oriented) may have a different effect on trust building in customer-supplier relationships, based on the first impression provided to potential customers. Although additional research is still needed, our findings add to the existing body of evidence that both cognitive and affective trust can be successfully fostered through online presence. Specifically, social media provides companies with tools to communicate benevolence to potential customer and, therefore, foster the affective commitment of customers. Traditional websites, instead, are more appropriate for communicating the competence and reliability of a company, by fostering trust building along the cognitive dimension. The results of our studies provide implications for researchers and practi-tioners, by highlighting the importance of combining the two media for effectively building a trustworthy online company image.

Today, people increasingly try to solve domain-specific problems through interaction on online Question and Answer (Q&A) sites, such as Stack Overflow. The growing success of the Stack Overflow community largely depends on the will of their members to answer others' questions. Recent research has shown that the factors that push members of online communities encompass both social and technical aspects. Yet, we argue that also the emotional style of a technical question does influence the probability of promptly obtaining a satisfying answer. In this paper, we describe the design of an empirical study aimed to investigate the role of affective lexicon on the questions posted in Stack Overflow.

We present a workshop in which trust in virtual teams is the central theme. Trust is essential for effective and efficient collaborations to take place and is more challenging when people are unable to meet face-to-face. The workshop aims to generate discussions which address three key issues within this general theme: 1) the factors that engender and inhibit trust, 2) the structure of a trust framework, 3) and the requirements for software tools that support the development of trust during virtual collaborations.