Joanna F. DeFranco

A cognitive collaborative model to improve performance in engineering teams—A study of team outcomes and mental model sharing

Working collaboratively in teams is an essential element in systems engineering: Interdisciplinary teams are formed to tackle large-scale, heterogeneous problems requiring skill and knowledge across a wide array of engineering and technical disciplines. While this is widely accepted as necessary, little attention is given to the problem of ensuring effective collaboration across the diverse team. Attention is given to the processes that will be followed, and tools are provided to aid communication; but the critical cognitive aspects that ensure that the team works effectively and efficiently towards a common objective are frequently absent. Instead, managers and team members assume that their disparate mental models have no impact on their collaborative efforts, or that any cognitive dissonance will evaporate naturally and organically. In reality, neither assumption is true, and if these issues are not directly addressed, a team will fall into cooperative rather than collaborative work, which is less effective and efficient. We introduce a framework, the Cognitive Collaborative Model, that explicitly promotes the cognitive collaborative processes necessary for effective engineering teams, and demonstrate its effectiveness in controlled system design and development experiments. Further, we investigate whether this improvement is due to convergence of the individual team member mental models into a shared, or team, mental model, often cited as the basis for high-performing teams. Finally, we propose a novel multistage evaluation process for mental model convergence using concept maps and Pathfinder analysis.

Toward a Student-Centered Measure of Learning Management System Utilization

Colleges and universities have spent significant financial and human resources deploying and promoting educational technologies, including Learning Management Systems (LMS). A large body of research now exists on the impact of technology on student learning, including the roles of blended learning, hybrid classes, and distance learning. Yet, despite all of this work, there are surprisingly few policy-focused tools available to assess whether these investments are paying off in the classroom. This article describes a student-centered measure of LMS utilization, average number of student logins per student, as a primary tool for policymakers. While no single measure of LMS utilization will ever answer all needs, the authors argue that a student-centered empirical measure could help move policy discussions forward in important ways. Complementary to theoretical models that focus on faculty adoption, a student-centered approach provides a basic measure of how often technology is being used by the learner. The article illustrates several ways in which the proposed empirical measure could be used to spur dialog about the use of academic technologies on campus.

The nonfunctional requirement focus in medical device software: a systematic mapping study and taxonomy

This paper describes the results and analysis of a systematic mapping study of research focusing on the nonfunctional requirements in software intensive medical devices. The review covered 238 journal papers from five digital libraries. The 55 papers that met the review inclusion criteria focused on 22 NFRs, each describing a unique system behavior quality. The most dominant of these NFRs were interoperability,usability,performance,security, privacy, safety, and accuracy. A noticeable NFR gap is the notion of caring. It is not readily apparent how a medical device that monitors a patient or delivers medications or anesthetics can “care about” the sufferings, feelings and emotional needs of a patient; however, in the healthcare arena these are valid concerns. A second theme found in the papers reviewed focused on software standards/process improvement when developing software intensive medical devices. This research also provides an analysis of the software architecture tactics those researchers utilized to implement the NFRs in the medical devices.

Software Testing: The State of the Practice

A Web-based survey examined how software professionals used testing. The results offer opportunities for further interpretation and comparison to software testers, project managers, and researchers. The data includes characteristics of practitioners, organizations, projects, and practices.

Improving collaborative learning in online software engineering education

ABSTRACT Team projects are commonplace in software engineering education. They address a key educational objective, provide students critical experience relevant to their future careers, allow instructors to set problems of greater scale and complexity than could be tackled individually, and are a vehicle for socially constructed learning. While all student teams experience challenges, those in fully online programmes must also deal with remote working, asynchronous coordination, and computer-mediated communications all of which contribute to greater social distance between team members. We have developed a facilitation framework to aid team collaboration and have demonstrated its efficacy, in prior research, with respect to team performance and outcomes. Those studies indicated, however, that despite experiencing improved project outcomes, students working in effective software engineering teams did not experience significantly improved individual achievement. To address this deficiency we implemented theoretically grounded refinements to the collaboration model based upon peer-tutoring research. Our results indicate a modest, but statistically significant (p = .08), improvement in individual achievement using this refined model.

A guidance framework for facilitating effective engineering student teams and its impact on individual learning

The ability of engineering students to work effectively in teams is a growing need expressed by both industry and academia. Consequently, an engineering student can expect to work on a number of teams during their studies and to have the outcomes of those efforts contribute meaningfully to their individual assessment. It is imperative, then, that those students are provided the guidance necessary to hone the skills of effective collaboration. We have developed a framework of guidance, the cognitive collaborative model, and report on it in this paper. Further, we demonstrate its efficacy in facilitating team mental model convergence and the concomitant improvement in team outcomes. Lastly, and critically, we have investigated whether those improved team experiences and outcomes lead to improvement in individual learning in accordance with the expectations from collaborative and social constructivist learning theories. Counter-intuitively, we find that this is not the case, and we report those results and suggest theoretical foundations for these findings.

A software engineering team research mapping study

Purpose The purpose of this mapping study has been performed to identify, critically analyze and synthesize research performed in the area of software engineering teams. Teams, in a general sense, have been studied extensively. But the distinctive processes that need to be executed effectively and efficiently in software engineering require a better understanding of current software engineering team research. Design/methodology/approach In this work, software engineering team publications were analyzed and the key findings of each paper that met our search inclusion criteria were synthesized. In addition, a keyword content analysis was performed to create a taxonomy to categorize each paper and evaluate the state of software engineering team research. Findings In software engineering team research, the resulting areas that are the most active are teamwork/collaboration, process/design and coordination. Clear themes of analysis have been determined to help understand how team members collaborate, factors affecting their success and interactions among all project stakeholders. In addition, themes related to tools to support team collaboration, improve the effectiveness of software engineering processes and support team coordination have been found. However, the research gaps determined from the content analysis point toward a need for more research in the area of communication and tools. Originality/value The goal of this work is to define the span of previous research in this area, create a taxonomy to categorize such research and identify open research areas to provide a clear road map for future research in the area of software engineering teams. These results, along with the key finding themes presented, will help guide future research in an area that touches all parts of the software engineering and development processes.

A content analysis process for qualitative software engineering research

A review of the qualitative research methods discussed in papers that study software engineering teams showed most of those papers did not follow a systematic process during the qualitative analysis. This finding is concerning as this deficiency in research analysis procedure may reduce the validity and/or completeness of the qualitative results. Such a lack of rigor may be a result of qualitative research not being as firmly established in software engineering as quantitative research methodologies. In engineering research, quantitative methods are typically more prevalent and qualitative analysis is part of a mixed-method analysis process. However, when researching teams, where human activity is abundant, qualitative analysis may need to take precedence. In this paper, we focus on the qualitative analysis method called content analysis with the goal of presenting a rigorous process for content analysis in the context of software engineering. We then present and demonstrate the use of that content analysis process for software engineering researchers using two examples. An analysis of 215 articles that were a result of a mapping study on software engineering team research is presented. Those papers were analyzed to determine which utilized a qualitative data analysis method in their research in addition to the rigor and type of qualitative analysis performed. We ultimately included 23 papers in this study. We present a mapping study and a content analysis process that include a straightforward way to select, code, and present data in both inductive and deductive studies. We demonstrated the process using the keywords from the papers included in this study as well as on a second data set that utilized responses from structured interview transcripts from practicing software engineers. The first dataset also resulted in a taxonomy to categorize software engineering team research. We presented and demonstrated a content analysis process in terms of software engineering in order to improve future qualitative software engineering research that would benefit from systematic content analysis.

Software Engineering of Safety-Critical Systems: Themes From Practitioners

This study addresses two important questions related to engineering of safety-critical software and software-intensive systems. The first question is: which software and software-intensive systems should be considered safety critical? The second question is: what processes, design practices, and tools have practitioners been using for building these systems? We answer these questions through an analysis of unstructured interviews with experienced engineers who self-describe as working on safety-critical systems. Then, a thematic analysis of these responses was conducted. The results of this study are intended to provide guidance to those building safety-critical systems and have implications on state engineering licensure boards, in the determination of legal liability, and in risk assessment for policymakers, corporate governors, and insurance executives.

Review and Analysis of Software Development Team Communication Research

Research problem: Communication affects many aspects of the software engineering process. In addition, poor team communication is often a root cause of failure for complex engineering projects. In global software engineering, communication and coordination become more challenging, and that fact affects the quality of the product. The goal of this study is to analyze the type and quality of research performed on software development team communication, and to present data to guide future research in these areas. Research questions: (1) How much communication research activity in the area of software development teams has been described in the literature from 2005 to 2015? (2) What is the current state of software development team communication research (what has been done and to what degree)? (3) What is the predominant research methodology of software development team communication research? (4) Where are the gaps in the current state of software development team communication research? (5) What are the major, common findings about communication in software development teams? Methodology: We reviewed 184 journal papers and performed a content analysis of the keywords from the relevant papers to create a software engineering team research taxonomy. We utilized this taxonomy to categorize the context of communication research papers. We then used the categorization results to determine the most active software development team communication research areas. In addition, we analyzed the quality of the journals (using impact factor and H-index as metrics) and the type of research performed in these areas (i.e., qualitative, quantitative, survey, social network analysis, or literature review). Results and conclusions: The results showed that the most active software development team communication research areas are global software development, project effectiveness, and effective teamwork. The most prevalent research methodology is a survey among those research areas. We conclude this paper with a presentation and discussion of the major findings of each research paper as well as the common themes among those findings in each of the top research areas.