In this paper, we study ways to enhance the composition of teams based on new requirements in a collaborative environment. We focus on recommending team members who can maintain the team's performance by minimizing changes to the team's skills and social structure. Our recommendations are based on computing team-level similarity, which includes skill similarity, structural similarity as well as the synergy between the two. Current heuristic approaches are one-dimensional and not comprehensive, as they consider the two aspects independently. To formalize team-level similarity, we adopt the notion of graph kernel of attributed graphs to encompass the two aspects and their interaction. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. Extensive empirical evaluations on real world datasets validate the effectiveness and efficiency of our algorithms.

The science of team science is a rapidly emerging research field that studies strategies to understand and enhance the process and outcomes of collaborative, team-based research. An interesting research question we address in this work is how to maintain and optimize the team performance should certain changes happen to the team. In particular, we take the network approach to understanding the teams and consider optimizing the teams with several operations (e.g., replacement, expansion, shrinkage). We develop TEAMOPT, a system to assist users in optimizing the team performance interactively to support the changes to a team. TEAMOPT takes as input a large network of individuals (e.g., co-author network of researchers) and is able to assist users in assembling a team with specific requirements and optimizing the team in response to the changes made to the team. It is effective in finding the best candidates, and interactive with users' feedback in the loop. The system is developed using HTML5, JavaScript, D3.js (front-end) and Python CGI (back-end). A prototype system is already deployed. We will invite the audience to experiment with our TEAMOPT in terms of its effectiveness, efficiency and applicability to various scenarios.

Cross-cultural network ties have been shown to improve decision-making, creativity, conflict-resolution and use of collaborative technologies. Nevertheless, cultural barriers are difficult to overcome. We used data from an internal Social Networking System (SNS) in a large global company to see if membership in the same company might reduce the effect of cultural homophily. We found no effect. However, when we focused on members of 87 distributed workgroups, we found that the effect of cultural differences actually reversed, indicating greater cultural diversity in online friendship ties than observed in the company at large. We discuss alternative explanations for this finding and the implications for work environments in global companies.

In this paper, we study the problem of TEAM MEMBER REPLACEMENT -- given a team of people embedded in a social network working on the same task, find a good candidate to best replace a team member who becomes unavailable to perform the task for certain reason (e.g., conflicts of interests or resource capacity). Prior studies in teamwork have suggested that a good team member replacement should bring synergy to the team in terms of having both skill matching and structure matching. However, existing techniques either do not cover both aspects or consider the two aspects independently. In this work, we propose a novel problem formulation using the concept of graph kernels that takes into account the interaction of both skill and structure matching requirements. To tackle the computational challenges, we propose a family of fast algorithms by (a) designing effective pruning strategies, and (b) exploring the smoothness between the existing and the new team structures. We conduct extensive experimental evaluations and user studies on real world datasets to demonstrate the effectiveness and efficiency. Our algorithms (a) perform significantly better than the alternative choices in terms of both precision and recall and (b) scale sub-linearly.

Leaders are important for the smooth functioning of teams but their effect on team performance is less well established. Based on a sample of 55 software development teams from two companies we examined the association between technical leaders- communication structure and the team's performance. Consistent with past research, we found that a team's productivity and quality improve when the team communication follows hierarchical and small-world patterns, respectively. However, there was significant additional improvement in both productivity and quality when technical leaders shared more information than they gathered, and when they were more central in the communication network. Surprisingly they were not necessarily the most "central" individuals in the team's communication structure. We discuss the implication of these results for new tools that provide feedback to leaders and the team

Online communities are increasingly being deployed in enterprises to increase productivity and share expertise. Community leaders are critical for fostering successful communities, but existing technologies rarely support leaders directly, both because of a lack of clear data about leader needs, and because existing tools are member- rather than leader-centric. We present the evidence-based design and evaluation of a novel tool for community leaders, Community Insights (CI). CI provides actionable analytics that help community leaders foster healthy communities, providing value to both members and the organization. We describe empirical and system contributions derived from a long-term deployment of CI to leaders of 470 communities over 10 months. Empirical contributions include new data showing: (a) which metrics are most useful for leaders to assess community health, (b) the need for and how to design actionable metrics, (c) the need for and how to design contextualized analytics to support sensemaking about community data. These findings motivate a novel community system that provides leaders with useful, actionable and contextualized analytics.

Finding the right person to ask for help is a difficult task within a large enterprise. While there are a few studies detailing practices for finding an expert often in the context of an expertise locator system, there are fewer studies on workplace practices and challenges for finding a person who can help, especially independent of any particular technology. We conducted a two-part study of helper-finding activities with 36 enterprise workers, representing different job roles and levels of experience. First, we present a taxonomy of workplace helper-finding needs that involves tasks, topics, and helper selection criteria, developed by analyzing two weeks of participant diaries describing helper-finding problems. Second, we present the results of follow-up interviews with each participant, focusing on helper-finding challenges in the workplace. Finally, we present design implications for systems aimed at supporting helper-finding in the workplace.

New product development teams face important challenges to their performance due to the novelty of the work and the need to rapidly develop shared knowledge and goals. However, little is known about the relationship between the structural properties of communication and performance in these teams. This study examined the effect of communication structure, specifically hierarchical and small-world structures on the delivery performance and quality outcomes of a large-scale new product development project. Our longitudinal analyses revealed that structuring communication patterns in a hierarchical manner significantly improves delivery performance. However, hierarchical communication has a detrimental effect on quality while small-world communication structures improved the quality outcomes of development teams. We discuss the implications of these results for collaborative tools that support communication tradeoffs

There is a growing body of research into the adoption and use of social software in enterprises. However, less is known about how groups, such as communities, use and appropriate these technologies, and the implications for community structures. In a study of 188 very active online enterprise communities, we found systematic differences in size, demographics and participation, aligned with differences in community types. Different types of communities differed in their appropriation of social software tools to create and use shared resources, and build relationships. We propose implications for design of community support features, services for potential community members, and organizations looking to derive value from online groups.

It is well established that distributed software projects benefit from informal communication. However, it is less clear how patterns of informal communication impact the performance of the individual developers. In a study of communication networks in a large commercial software project, we found that individuals performed better when they were central within a team's communication network but their performance worsened if they were central within the communication for the whole project. On the other hand, individuals embedded in a dense communication cluster at the team and at the project level perform better than those who were not embedded. The effects for both network positions were maintained even after controlling for formal role, individual differences in communication, workload and other factors that drive communication. We discuss the implications of the results for intra- and inter-team communication and for the inclusion of network structure into the design of collaborative and awareness tools.

Research on intelligent systems has emphasized the benefits of providing explanations along with recommendations. But can explanations lead users to make incorrect decisions? We explored this question in a controlled experimental study with 18 professional network security analysts doing an incident classification task using a prototype cybersecurity system. The system provided three recommendations on each trial. The recommendations were displayed with explanations (called "justifications") or without. On half the trials, one of the recommendations was correct; in the other half none of the recommendations was correct. Users were more accurate with correct recommendations. Although there was no benefit overall of explanation, we found that a segment of the analysts were more accurate with explanations when a correct choice was available but were less accurate with explanations in the absence of a correct choice. We discuss implications of these results for the design of intelligent systems.

OFTWARE engineering has long been recognized as a human activity that is managed through a system of methods, tools, processes, and work practices. This interaction between the nature of the product, the work, and the people doing it represents the socio-technical environment of a software development project. Software projects that fail to adequately consider the people’s social behavior in the context of the product and the work are less likely to be successful [2], [3]. The negative consequences of a neglected socio-technical environment are even more evident in geographically distributed software development, a project setting that has become commonplace in the last few decades. A growing body of research has adopted the sociotechnical perspective. However, the research is currently dispersed across multiple venues, including computersupported cooperative work (CSCW), computer-human interaction (CHI), software engineering, and organizational theory and behavior. As a first attempt to integrate these strands of research for software engineering, we organized workshops at ICSE 2008 and ICSE 2009. This special section builds on the success of those workshops and highlights emerging and promising lines of research that have the potential to shape future research and practice in software

Social software is built around an "architecture of participation" where user data is aggregated as a side-effect of using Web 2.0 applications. Web 2.0 implies that processes and tools are socially open, and that content can be used in several different contexts. Web 2.0 tools and technologies support interactive information sharing, data interoperability and user centered design. For instance, wikis, blogs, tags and feeds help us organize, manage and categorize content in an informal and collaborative way. One goal of this workshop is to investigate how these technologies can improve software development practices. Some of these technologies have made their way into collaborative software development processes such as Agile and Scrum, and in development platforms such as Rational Team Concert which draw their inspiration from Web 2.0. These processes and environments are just scratching the surface of what can be done by incorporating Web 2.0 approaches and technologies into collaborative software development. This workshop aims to improve our understanding of how Web 2.0, manifested in technologies such as mashups or dashboards, can change the culture of collaborative software development.

Analysts engaged in real-time monitoring of cybersecurity incidents must quickly and accurately respond to alerts generated by intrusion detection systems. We investigated two complementary approaches to improving analyst performance on this vigilance task: a graph-based visualization of correlated IDS output and defensible recommendations based on machine learning from historical analyst behavior. We tested our approach with 18 professional cybersecurity analysts using a prototype environment in which we compared the visualization with a conventional tabular display, and the defensible recommendations with limited or no recommendations. Quantitative results showed improved analyst accuracy with the visual display and the defensible recommendations. Additional qualitative data from a "talk aloud" protocol illustrated the role of displays and recommendations in analysts' decision-making process. Implications for the design of future online analysis environments are discussed.

Abstract Building on the major premises of transactive memory (TM) theory as well as the recent multilevel extension to the original theory, this study examined the influence of perceived social accessibility of expertise providers, technological accessibility, and awareness of expertise distribution on expertise retrieval. Using social network data collected from a large global sales team, the study found that all three variables had significant impact on expertise retrieval at both the dyadic and individual levels. Our study confirmed the conceptual and theoretical value of approaching TM from a multilevel network perspective.

Contemporary work increasingly involves interacting with strangers in technology-mediated environments. In this context, we come to rely on digital artifacts to infer characteristics of other people. This paper reports the results of a study conducted in a global company that used expertise search as a vehicle for exploring how people interpret a range of information available in online profiles in evaluating whom to interact with for expertise. Using signaling theory as a conceptual framework, we describe how certain 'signals' in various social software are hard to fake, and are thus more reliable indicators of expertise. Multi-level regression analysis revealed that participation in social software, social connection information, and self-described expertise in the corporate directory were significantly helpful in the decision to contact someone for expertise. Qualitative analysis provided further insights regarding the interpretations people form of others' expertise from digital artifacts. We conclude with suggestions on differentiating various types of information available within online profiles and implications for the design of expertise locator/recommender systems.

It is well established that there is a need to find experts to get answers or advice. A variety of expertise locator tools have emerged to help locate the right person. But there is little systematic study on what people are really looking for when such systems are used and how external factors such as job role may shape that search. We conducted a study of 75 employees who were current users of an expertise locator system. An analysis of the reasons for their search revealed that people in client facing roles are primarily seeking to have a dialog with an expert, while others are just as likely to seek answers to technical questions. We also surveyed various tools for finding experts and found that corporate directories and personal networks were most often cited as alternatives to an expertise locator. We discuss the implications of these results for the design of tools for finding experts and expert knowledge.

Expertise locator systems have been designed to help find experts within organizations. While there are many examples of these systems in the literature, there has not been any systematic analysis of the factors that predict whether a particular expertise search result will be selected for further exploration. This paper describes a study of 67 employees from 21 countries that performed a specific expertise search to find an expert using an expertise locator system. Rank order and social connection information displayed in snippets of search results were found to significantly predict whether a user considers a particular search result for further exploration. Implications for the design of expertise location systems and future research directions are discussed.

Employees depend on other people in the enterprise for rapid access to important information. But current systems for finding experts do not adequately address the social implications of finding and engaging strangers in conversation. This paper provides a user study of SmallBlue, a social-context-aware expertise search system that can be used to identify experts, see dynamic profile information and get information about the social distance to the expert, before deciding whether and how to initiate contact. The system uses an innovative approach to privacy to infer content and dynamic social networks from email and chat logs. We describe usage of SmallBlue and discuss implications for the next generation of enterprise-wide systems for finding people.

One of the pervasive challenges facing any software development team is getting the right level and timing of communication to ensure that people are able to coordinate their work effectively. Communication issues are difficult to address because important aspects of communication are largely invisible to both management and the individuals on the team. Communication challenges are further exaggerated in global software teams, because of the different time-zones, cultures, and working environments. Social Network Analysis (SNA) is an established method for revealing patterns of human communication and decision-making. This tutorial introduces students to basic concepts in SNA, illustrates how SNA can be used to understand the dynamics of and address common communication problems in global software teams, and provides structured exercises in data capture, analysis and interpretation.

In order to take advantage of lower costs and wider availability of talent, managers often staff software development projects globally. While this practice may be economically appealing there are often hidden costs. This paper reports the results of a study that used social network analysis to study how people in three global software teams acquired and used available expertise through communication and acquisition strategies. We found that frequency of communication was associated with awareness and familiarity of the other person especially awareness of the person's current work. When it came to acquiring information, members of these teams were more likely to seek specific technical information or administrative help from people outside their software team. People use others on their team with whom they have strong ties to exploit preexisting knowledge but they go to people they know uniquely outside the team for innovative ideas. We discuss the implications of these results for tools that encourage transparent work and for work and management practices

Collaborative filtering is based on the premise that people looking for information should be able to make use of what others have already found and evaluated.Current collaborative filtoing systems provide tools for readers to filter documents based on aggregated ratings over a changing group of readers.Motivated by the results of a study of information sharing, we describe a different type of collaborative filtering system in which people who find interesting documents actively send "pointers" to those documents to their colleagues.A "pointer" contains a hypertext link to the source document as well as contextual information to help the recipient determine the interest and relevance of the document prior to accessing it.Preliminary data suggest that people are using the system in anticipated and unanticipated ways, as well as creating information "digests".

No abstract available.

Hypertext offers users a simple, flexible way to navigate through electronic information systems but at the potential risk of becoming lost in the network of interconnected pieces of information. A study was conducted on information retrieval using a commercial hypertext based help system. It was found that the predominant search strategy was “browsing” (characterized by scanning tables of contents and paging through topics), rather than employing the indexes (“analytical search”). Although subjects did not become lost, individuals with better spatial visualization ability, as measured by a standardized test, were faster at retrieving information and returned to the top of the information hierarchy less often than those with poorer spatial visualization ability. These results support previous studies that have found a strong preference by users to browse in hypertext systems and extend those findings to a new domain (help), a different type of user interface, and a different information architecture. In addition, the results demonstrate the importance of spatial visualization ability for efficient navigation and information retrieval in a hierarchical hypertext system.

Hypertext offers users a simple, flexible way to navigate through electronic information systems but at the potential risk of becoming lost in the network of interconnected pieces of information. A study was conducted on information retrieval using a commercial hypertext-based help system. It was found that the predominant search strategy was “browsing” (characterized by scanning tables of contents and paging through topics), rather than employing the indexes ("analytical search"). Although subjects did not get lost, individuals with better spatial visualization skills, as measured by a standardized test, were faster at retrieving information and returned to the top of the information hierarchy less often than those with poorer spatial visualization skills. These results support previous studies that have found a strong preference by users for browsing in hypertext systems and extend those findings to a new domain (help), a different type of user interface, and a different information architecture. In addition, the results demonstrate the importance of spatial visualization ability for efficient navigation and information retrieval in a hierarchical hypertext system.

Research issues concerning human interaction with computers have become crucial because of dramatic increases in the number of computer users who have no formal mathematical, engineering, or computer science training. This review focuses on the state of the art in Software Psychology--the study of human factors in computer systems. One area of software psychology, the psychology of programming, is picked for an in-depth discussion of research results. This area was selected for its interest to the readers of the PROCEEDINGS OF THE IEEE. The review then discusses the methodological issues involved in performing experimental research in software psychology. Future trends are discussed both for the programming research reviewed here and for the whole field of software psychology. The conclusions identify multidisciplinary training and research as critical to further progress in this field.

We suggest that expert programmers have and use two types of programming knowledge: 1) programming plans, which are generic program fragments that represent stereotypic action sequences in programming, and 2) rules of programming discourse, which capture the conventions in programming and govern the composition of the plans into programs. We report here on two empirical studies that attempt to evaluate the above hypothesis. Results from these studies do in fact support our claim.

While programmers may differ in their assessment of the comprehensibility of a program, there are nonetheless some clear cut cases of programs that are truly difficult to understand. In this paper, we analyze three programs—two of which are relatively incomprehensible—using Halstead's Volume Metric, Propositional Analysis and Plan Analysis. We argue that only Plan Analysis provides a satisfactory explanation for why the programs in question differ with respect to understandability. Moreover, we suggest that a qualitative analysis, such as provided by Plan Analysis, is the desired type of evaluation: rather than simply providing a numerical ranking for programs, the qualitative analysis can pinpoint the troublesome area in the code and provide prescriptive information for correcting the difficulty.

The Cognition and Programming Group at Yale University is engaged in two complementary efforts:

The qusstlun of how people resolve pronouns has the various factors combine.