Automated text-based analysis for decision-making research

We present results from a study on constructing and evaluating a support tool for the extraction of patterns in distributed decision -making processes, based on design criteria elicited from a study on the work process involved in studying such decision-making. Specifically, we devised and evaluated an analysis tool for C² researchers who study simulated decision-making scenarios for command teams. The analysis tool used text clustering as an underlying pattern extraction technique and was evaluated together with C² researchers in a workshop to establish whether the design criteria were valid and the approach taken with the analysis tool was sound. Design criteria elicited from an earlier study with researchers (open-endedness and transparency) were highly consistent with the results from the workshop. Specifically, evaluation results indicate that successful deployment of advanced analysis tools requires that tools can treat multiple data sources and offer rich opportunities for manipulation and interaction (open-endedness) and careful design of visual presentations and explanations of the techniques used (transparency). Finally, the results point to the high relevance and promise of using text clustering as a support for analysis of C² data.     

A tool supporting root cause analysis for synchronous retrospectives in distributed software teams

ContextRoot cause analysis (RCA) is a useful practice for software project retrospectives, and is typically carried out in synchronous collocated face-to-face meetings. Conducting RCA with distributed teams is challenging, as face-to-face meetings are infeasible. Lack of adequate real-time tool support exacerbates this problem. Furthermore, there are no empirical studies on using RCA in synchronous retrospectives of geographically distributed teams.ObjectiveThis paper presents a real-time cloud-based software tool (ARCA-tool) we developed to support RCA in distributed teams and its initial empirical evaluation. The feasibility of using RCA with distributed teams is also evaluated.MethodWe compared our tool with 35 existing RCA software tools. We conducted field studies of four distributed agile software teams at two international software product companies. The teams conducted RCA collaboratively in synchronous retrospective meetings by using the tool we developed. We collected the data using observations, interviews and questionnaires.ResultsComparison revealed that none of the existing 35 tools matched all the features of our ARCA-tool. The team members found ARCA-tool to be an essential part of their distributed retrospectives. They considered the software as efficient and very easy to learn and use. Additionally, the team members perceived RCA to be a vital part of the retrospectives. In contrast to the prior retrospective practices of the teams, the introduced RCA method was evaluated as efficient and easy to use.ConclusionRCA is a useful practice in synchronous distributed retrospectives. However, it requires software tool support for enabling real-time view and co-creation of a cause-effect diagram. ARCA-tool supports synchronous RCA, and includes support for logging problems and causes, problem prioritization, cause-effect diagramming, and logging of process improvement proposals. It enables conducting RCA in distributed retrospectives.     

Co-Insights framework for collaborative decision support and tacit knowledge transfer

Abundant software tools use visual analytics (VA) techniques to support various decisions with the aim of boosting better insights. Large organizations, however, lose efficiency in selecting the right tools to support the persons who apply the tools to various decision tasks. Consequently, the creation and sharing of insights are far from optimal, leading consistently to sub-optimal decisions. In this work, the Co-Insights framework is introduced with automated collaboration support features to enable effective creation and sharing of distributed insights. A collaboration network (Co-Net) is established to model the collaborative decision making process in an organization. Two important features of the Co-Insights framework are developed: collaborative agent allocation analysis (CA3) for task–participant matching; and a robust mechanism for the recommendation of selected VA tools, by participant–tool matching. Thus, by better matching of tasks and tools with participants, the creation and sharing of insights are improved in any collaborative team for better decision making, accompanied with the tacit knowledge transfer to sustain the entire organization. To validate the effectiveness of these two main features, two experiments built on the Co-Net model are performed to test the newly developed algorithms. It has been found that CA3 significantly improves the matching scores by up to 35%, compared with conventional task–participant matching methods. The neural network based participant–tool matching mechanism yields robust results with 4% mismatches for 10% noise levels, and with 16% mismatches for 30% noise levels. Real case applications and implications are described, and further plans to extend this new framework are also outlined based on the reported experiments and evaluations.     

Tools used in Global Software Engineering: A systematic mapping review

ContextThis systematic mapping review is set in a Global Software Engineering (GSE) context, characterized by a highly distributed environment in which project team members work separately in different countries. This geographic separation creates specific challenges associated with global communication, coordination and control.ObjectiveThe main goal of this study is to discover all the available communication and coordination tools that can support highly distributed teams, how these tools have been applied in GSE, and then to describe and classify the tools to allow both practitioners and researchers involved in GSE to make use of the available tool support in GSE.MethodWe performed a systematic mapping review through a search for studies that answered our research question, “Which software tools (commercial, free or research based) are available to support Global Software Engineering?” Applying a range of related search terms to key electronic databases, selected journals, and conferences and workshops enabled us to extract relevant papers. We then used a data extraction template to classify, extract and record important information about the GSD tools from each paper. This information was synthesized and presented as a general map of types of GSD tools, the tool’s main features and how each tool was validated in practice.ResultsThe main result is a list of 132 tools, which, according to the literature, have been, or are intended to be, used in global software projects. The classification of these tools includes lists of features for communication, coordination and control as well as how the tool has been validated in practice. We found that out the total of 132, the majority of tools were developed at research centers, and only a small percentage of tools (18.9%) are reported as having been tested outside the initial context in which they were developed.ConclusionThe most common features in the GSE tools included in this study are: team activity and social awareness, support for informal communication, Support for Distributed Knowledge Management and Interoperability with other tools. Finally, there is the need for an evaluation of these tools to verify their external validity, or usefulness in a wider global environment.     

Empirical evaluation of a decision support model for adopting software product line engineering

ContextThe software product line engineering (SPLE) community has provided several different approaches for assessing the feasibility of SPLE adoption and selecting transition strategies. These approaches usually include many rules and guidelines which are very often implicit or scattered over different publications. Hence, for the practitioners it is not always easy to select and use these rules to support the decision making process. Even in case the rules are known, the lack of automated support for storing and executing the rules seriously impedes the decision making process.ObjectiveWe aim to evaluate the impact of a decision support system (DSS) on decision-making in SPLE adoption. In alignment with this goal, we provide a decision support model (DSM) and the corresponding DSS.MethodFirst, we apply a systematic literature review (SLR) on the existing primary studies that discuss and present approaches for analyzing the feasibility of SPLE adoption and transition strategies. Second, based on the data extraction and synthesis activities of the SLR, the required questions and rules are derived and implemented in the DSS. Third, for validation of the approach we conduct multiple case studies.ResultsIn the course of the SLR, 31 primary studies were identified from which we could construct 25 aspects, 39 questions and 312 rules. We have developed the DSS tool Transit-PL that embodies these elements.ConclusionsThe multiple case study validation showed that the adoption of the developed DSS tool is justified to support the decision making process in SPLE adoption.     

Development of fuzzy process accuracy index for decision making problems

Several statistical decision making tools and methods are available to organize evidence, evaluate risks, and aid in decision making. Process capability indices are the summary statistics to point out the process performance. In this paper, these indices are analyzed to obtain a new decision making tool. Process accuracy index (C_a) measures the degree of process centering and gives alerts when the process mean departures from the target value. It focuses on the location of process mean and the distance between mean and target value. We modify the traditional process accuracy index to obtain a new tool under fuzziness. With the proposed tool, specification limits and process mean can be defined as triangular or trapezoidal fuzzy numbers. The proposed tool is illustrated to solve a supplier selection problem.     

Unanimously acceptable agreements for negotiation teams in unpredictable domains

A negotiation team is a set of agents with common and possibly also conflicting preferences that forms one of the parties of a negotiation. A negotiation team is involved in two decision making processes simultaneously, a negotiation with the opponents, and an intra-team process to decide on the moves to make in the negotiation. This article focuses on negotiation team decision making for circumstances that require unanimity of team decisions. Existing agent-based approaches only guarantee unanimity in teams negotiating in domains exclusively composed of predictable and compatible issues. This article presents a model for negotiation teams that guarantees unanimous team decisions in domains consisting of predictable and compatible, and alsounpredictable issues. Moreover, the article explores the influence of using opponent, and team member models in the proposing strategies that team members use. Experimental results show that the team benefits if team members employ Bayesian learning to model their teammates’ preferences.     

The role of artificial intelligence in understanding the strategicdecision-making process

A survey is given which focuses on research issues involving strategic planning and artificial intelligence (AI), including the nature of the formulation task, cognitive studies of strategic planners and computer-based support for strategic planning. The author reviews the research to date, and argues that, like traditional decision support systems (DSS) research, much of the potential for future research in this area lies in modeling the ill-structured, early stages of the strategic decision making process namely, the strategic intelligence analysis and the issue diagnosis. Therefore, he discusses a specific model-based approach to the study of these early stages. Research in artificial intelligence-including investigations into diagnosis and situation assessment, analogical reasoning, plan recognition, nonmonotonic reasoning and distributed intelligence, among others-can be used to build models of strategic decision making that help researchers in better understanding this traditionally unstructured activity     

Decision support systems: Alternative perspectives and schools

This paper reviews and compares four distinct decision support system (DSS) perspectives or ‘schools’: Decision analysis, decision research, decision calculus, and implementation process. Each school represents a relatively coherent perspective for DSS development; all four address the development and use of computer-based tools that support and aid managers in their role as decision makers. They differ, however, in terms of the nature of the decision situation envisaged, the phase of the decision process considered, the primary aims for a DSS development effort, the nature of the learning to be achieved and the phase of the development process that is emphasized. The purpose of the review is an attempt to establish a more constructive approach to understanding the central tenets and challenges for DSS. At the same time, the unique concerns and distinct contributions of the different schools suggest that it is difficult to define a single approach that satisfies equally well all considerations and all demands.     

Geovisualization for knowledge construction and decision support

Geovisualization is both a process for leveraging the data resources to meet scientific and societal needs and a research field that develops visual methods and tools to support a wide array of geospatial data applications. While researchers have made substantial advances in geovisualization over the past decade, many challenges remain. To support real-world knowledge construction and decision making, some of the most important challenges involve distributed geovisualization - that is, enabling geovisualization across software components, devices, people, and places.     

A group decision making support system in logistics and supply chain management

PurposeThe paper proposes a decision support system for selecting logistics providers based on the quality function deployment (QFD) and the technique for order preference by the similarity to ideal solution (TOPSIS) for agricultural supply chain in France. The research provides a platform for group decision making to facilitate decision process and check the consistency of the outcomes.MethodologyThe proposed model looks at the decision problem from two points of view considering both technical and customer perspectives. The main customer criteria are confidence in a safe and durable product, emission of pollutants and hazardous materials, social responsibility, etc. The main technical factors are financial stability, quality, delivery condition, services, etc. based on the literature review. The second stage in the adopted methodology is the combination of quality function deployment and the technique for order preference by similarity to ideal solution to effectively analyze the decision problem. In final section we structure a group decision system called GRoUp System (GRUS) which has been developed by Institut de Recherche en Informatique de Toulouse (IRIT) in the Toulouse University.ResultsThis paper designs a group decision making system to interface decision makers and customer values in order to aid agricultural partners and investors in the selection of third party logistic providers. Moreover, we have figured out a decision support system under fuzzy linguistic variables is able to assist agricultural parties in uncertain situations. This integrated and efficient decision support system enhances quality and reliability of the decision making.Novelty/OriginalityThe novelty of this paper is reflected by several items. The integration of group multi-criteria decision tools enables decision makers to obtain a comprehensive understanding of customer needs and technical requirements of the logistic process. In addition, this investigation is carried out under a European commission project called Risk and Uncertain Conditions for Agriculture Production Systems (RUC-APS) which models risk reduction and elimination from the agricultural supply chain. Ultimately, we have implemented the decision support tool to select the best logistic provider among France logistics and transportation companies.     

Work Environment and the Bottom Line: Survey of Tools Relating Work Environment to Business Results

Insufficient knowledge of how the working environment (WE) impacts company performance leads to a poor basis for managerial decision making. The objective of this study was to survey and evaluate available tools relating WE to business results. Nine tools were identified and qualitatively evaluated using 11 criteria. The tools were clustered into three categories, ranging from analysis of WE risks as well as calculation of their economic impact to solely investment analysis. Shortcomings in the tools include insufficient attention to quality and productivity issues and insufficient guidance for users. Although further methodological development is indicated, researchers should also attend to the factors affecting tool uptake and application as well as the potential to integrate such analysis into companies' regular accounting systems. © 2011 Wiley Periodicals, Inc.     

Acquiring and sharing tacit knowledge in software development teams: An empirical study

ContextSharing expert knowledge is a key process in developing software products. Since expert knowledge is mostly tacit, the acquisition and sharing of tacit knowledge along with the development of a transactive memory system (TMS) are significant factors in effective software teams.ObjectiveWe seek to enhance our understanding human factors in the software development process and provide support for the agile approach, particularly in its advocacy of social interaction, by answering two questions: How do software development teams acquire and share tacit knowledge? What roles do tacit knowledge and transactive memory play in successful team performance?MethodA theoretical model describing the process for acquiring and sharing tacit knowledge and development of a TMS through social interaction is presented and a second predictive model addresses the two research questions above. The elements of the predictive model and other demographic variables were incorporated into a larger online survey for software development teams, completed by 46 software SMEs, consisting of 181 individual team members.ResultsOur results show that team tacit knowledge is acquired and shared directly through good quality social interactions and through the development of a TMS with quality of social interaction playing a greater role than transactive memory. Both TMS and team tacit knowledge predict effectiveness but not efficiency in software teams.ConclusionIt is concluded that TMS and team tacit knowledge can differentiate between low- and high-performing teams in terms of effectiveness, where more effective teams have a competitive advantage in developing new products and bringing them to market. As face-to-face social interaction is key, collocated, functionally rich, domain expert teams are advocated rather than distributed teams, though arguably the team manager may be in a separate geographic location provided that there is frequent communication and effective use of issue tracking tools as in agile teams.     

Representational Reasoning and Verification

Formal approaches to the design of interactive systems rely on reasoning about properties of the system at a very high level of abstraction. Specifications to support such an approach typically provide little scope for reasoning about presentations and the representation of information in the presentation. In contrast, psychological theories such as distributed cognition place a strong emphasis on the role of representations, and their perception by the user, in the cognitive process. However, the post-hoc techniques for the observation and analysis of existing systems which have developed out of the theory do not help us in addressing such issues at the design stage. Mn this paper we show how a formalisation can be used to investigate the representational aspects of an interface. Our goal is to provide a framework to help identify and resolve potential problems with the representation of information, and to support understanding of representational issues in design. We present a model for linking properties at the abstract and perceptual levels, and illustrate its use in a case study of a ight deck instrument. There is a widespread consensus that proper tool support is a prerequisite for the adoption of formal techniques, but the use of such tools can have a profound effect on the process itself. In order to explore this issue, we apply a higher-order logic theorem prover to the analysis. Received May 1999 / Accepted in revised form July 2000     

Making the leap to a software platform strategy: Issues and challenges

ContextWhile there are many success stories of achieving high reuse and improved quality using software platforms, there is a need to investigate the issues and challenges organizations face when transitioning to a software platform strategy.ObjectiveThis case study provides a comprehensive taxonomy of the challenges faced when a medium-scale organization decided to adopt software platforms. The study also reveals how new trends in software engineering (i.e. agile methods, distributed development, and flat management structures) interplayed with the chosen platform strategy.MethodWe used an ethnographic approach to collect data by spending time at a medium-scale company in Scandinavia. We conducted 16 in-depth interviews with representatives of eight different teams, three of which were working on three separate platforms. The collected data was analyzed using Grounded Theory.ResultsThe findings identify four classes of challenges, namely: business challenges, organizational challenges, technical challenges, and people challenges. The article explains how these findings can be used to help researchers and practitioners identify practical solutions and required tool support.ConclusionThe organization’s decision to adopt a software platform strategy introduced a number of challenges. These challenges need to be understood and addressed in order to reap the benefits of reuse. Researchers need to further investigate issues such as supportive organizational structures for platform development, the role of agile methods in software platforms, tool support for testing and continuous integration in the platform context, and reuse recommendation systems.     

The user action framework: a reliable foundation for usability engineering support tools

Although various methods exist for performing usability evaluation, they lack a systematic framework for guiding and structuring the assessment and reporting activities. Consequently, analysis and reporting of usability data are ad hoc and do not live up to their potential in cost effectiveness, and usability engineering support tools are not well integrated. We developed the User Action Framework, a structured knowledge base of usability concepts and issues, as a framework on which to build a broad suite of usability engineering support tools. The User Action Framework helps to guide the development of each tool and to integrate the set of tools in the practitioner's working environment. An important characteristic of the User Action Framework is its own reliability in term of consistent use by practitioners. Consistent understanding and reporting of the underlying causes of usability problems are requirements for cost-effective analysis and redesign. Thus, high reliability in terms of agreement by users on what the User Action Framework means and how it is used is essential for its role as a common foundation for the tools. Here we describe how we achieved high reliability in the User Action Framework, and we support the claim with strongly positive results of a summative reliability study conducted to measure agreement among 10 usability experts in classifying 15 different usability problems. Reliability data from the User Action Framework are also compared to data collected from nine of the same usability experts using a classic heuristic evaluation technique.     

The effect of governance on global software development: An empirical research in transactive memory systems

ContextThe way global software development (GSD) activities are managed impacts knowledge transactions between team members. The first is captured in governance decisions, and the latter in a transactive memory system (TMS), a shared cognitive system for encoding, storing and retrieving knowledge between members of a group.ObjectiveWe seek to identify how different governance decisions (such as business strategy, team configuration, task allocation) affect the structure of transactive memory systems as well as the processes developed within those systems.MethodWe use both a quantitative and a qualitative approach. We collect quantitative data through an online survey to identify transactive memory systems. We analyze transactive memory structures using social network analysis techniques and we build a latent variable model to measure transactive memory processes. We further support and triangulate our results by means of interviews, which also help us examine the GSD governance modes of the participating projects. We analyze governance modes, as set of decisions based on three aspects; business strategy, team structure and composition, and task allocation.ResultsOur results suggest that different governance decisions have a different impact on transactive memory systems. Offshore insourcing as a business strategy, for instance, creates tightly-connected clusters, which in turn leads to better developed transactive memory processes. We also find that within the composition and structure of GSD teams, there are boundary spanners (formal or informal) who have a better overview of the network’s activities and become central members within their network. An interesting mapping between task allocation and the composition of the network core suggests that the way tasks are allocated among distributed teams is an indicator of where expertise resides.ConclusionWe present an analytical method to examine GSD governance decisions and their effect on transactive memory systems. Our method can be used from both practitioners and researchers as a “cause and effect” tool for improving collaboration of global software teams.     

Information Sharing Among Disaster Responders - An Interactive Spreadsheet-Based Collaboration Approach

Recent natural disasters have led crisis management organizations to revise their protocols so as to rely on the contribution of a wider range of actors, including simple citizens as well as expert operators, to support decision making activities. Reliable and timely information sharing among members of distributed teams of disaster responders has become paramount for the success of the overall crisis management process. In this paper we propose a crisis management system based on spreadsheet-mediated collaboration among on-site responders and decision makers. To share data a common spreadsheet artifact has been developed by using a participatory design approach which is accessed through mobile user interfaces. The evaluation results showed that the use of the spreadsheet artifact has resulted in more effective decision making relating to set of earthquake management scenarios in high-risk areas located in Italy.