Understanding agile software development practices using shared mental models theory

ContextAgile software development is an alternative software development methodology that originated from practice to encourage collaboration between developers and users, to leverage rapid development cycles, and to respond to changes in a dynamic environment. Although agile practices are widely used in organizations, academics call for more theoretical research to understand the value of agile software development methodologies.ObjectiveThis study uses shared mental models theory as a lens to examine practices from agile software methodologies to understand how agile practices enable software development teams to work together to complete tasks and work together effectively as a team.MethodA conceptual analysis of specific agile practices was conducted using the lens of shared mental models theory. Three agile practices from Xtreme Programming and Scrum are examined in detail, system metaphor, stand-up meeting, and on-site customer, using shared mental models theory.ResultsExamining agile practices using shared mental models theory elucidates how agile practices improve collaboration during the software development process. The results explain how agile practices contribute toward a shared understanding and enhanced collaboration within the software development team.ConclusionsThis conceptual analysis demonstrates the value of agile practices in developing shared mental models (i.e. shared understanding) among developers and customers in software development teams. Some agile practices are useful in developing a shared understanding about the tasks to be completed, while other agile practices create shared mental models about team processes and team interactions. To elicit the desired outcomes of agile software development methods, software development teams should consider whether or not agile practices are used in a manner that enhances the team’s shared understanding. Using three specific agile practices as examples, this research demonstrates how theory, such as shared mental models theory, can enhance our understanding regarding how agile practices are useful in enhancing collaboration in the workplace.     

Improving task breakdown comprehensiveness in agile projects with an Interaction Room

ContextThe planning, estimation and controlling mechanisms of agile process models rely significantly on a fixed set of tasks being established for each sprint. These tasks are created as refinements of product backlog items at the beginning of each sprint. However, a project team's understanding of the backlog items’ business implications and dependencies may often not be deep enough to identify all necessary tasks this early, so in addition to the tasks defined in the beginning of the sprint, more necessary tasks might be discovered as the sprint progresses, making any attempt at progress estimation or risk management difficult.ObjectiveWe strive to enable software teams to achieve a deeper understanding of product backlog items, which should help them to identify a sprint's tasks more reliably and comprehensively, and avoid discovering the need for extra tasks during sprint execution.MethodWe introduced a project team in a medium-sized software development company to the Interaction Room method, which encourages interdisciplinary communication about key system design aspects among all stakeholders. We observed the team's conduct in the sprint planning meetings, and tracked early- vs. late-identified tasks across several sprints.ResultsBefore the introduction of our method, the team used to discover on average 26% of a sprint's tasks not at the beginning of the sprint, but later during the course of the sprint. Using the Interaction Room in two separate projects, this ratio dropped to an average of 5% late-discovered tasks.ConclusionOur observations from these projects suggest that increased communication among all stakeholders of a project leads to a more reliable identification of the tasks to be performed in a sprint, and that an Interaction Room can provide appropriate guidance to conduct this team communication in a focused and pragmatic way.     

Augmented places: An impact of embodied historical experience on attitudes towards places

AimThe purpose of this paper was to examine the possibility of using Augmented Reality to reduce memory-related ethnic bias towards places and to increase understanding of multicultural place meaning and change emotional attitudes towards historical sites.BackgroundTheoretical and empirical works suggest that the direct, embodied contact with place's history facilitates understanding of its past. Places which were deprived of historical continuity are less liked and threatened by ethnic bias in collective memory. AR technology gives a possibility of recreating past of such places and in a consequence tests an influence of experience of place's history on psychological aspects of the people-place relationship.MethodA between-subjects experiment was conducted, in which participants either walked with an AR application displaying historical photos in real environment or watched photos on computers. The chosen place was the former Jewish district in Warsaw, Poland.ResultsThe results of multiple regression analyses showed that the AR application can facilitate positive attitudes towards a place, reduce ethnic bias and enhance multicultural place meaning.ConclusionsWe argue that AR could be used as a method of reviving (multi)cultural heritage, but also as a tool of reducing prejudices and increasing openness to other cultures and traditions.     

Task-specific visual cues for improving process model understanding

ContextBusiness process models support various stakeholders in managing business processes and designing process-aware information systems. In order to make effective use of these models, they have to be readily understandable.ObjectivePrior research has emphasized the potential of visual cues to highlight relevant matters in models such that stakeholders can use them more efficiently. What prior research does not explain is in how far visual cues can be customized to specific understanding tasks and how this influences cognition.MethodIn this paper, we address these questions with an experimental research design, in which we use eye-tracking equipment to capture how process experts use models to answer comprehension questions. As a treatment, we designed two manipulations of the secondary notation, namely coloring and layout, to direct attention to the elements relevant for the specific tasks.ResultsOur results indicate that both manipulations improve both eye-tracking-based measures and performance measures such as duration and efficiency, with color having the stronger effect.ConclusionsOur findings lay the foundation for novel features of process modeling tools that provide modifications of secondary notation in response to specific user queries. More generally, our research emphasizes the importance of the relevant region associated with a particular model understanding task.     

Evaluation of quality and personalisation of VR/AR/MR learning systems

ABSTRACT

The paper aims to analyse the problem of quality evaluation and personalisation of virtual reality/augmented reality/mixed reality (VR/AR/MR). First of all, systematic review of relevant scientific literature on the research topic was conducted. After that, findings of the systematic review concerning evaluation of quality and personalisation of VR/AR/MR learning environments are presented. The author’s VR/AR/MR learning systems/environments quality evaluation and personalisation framework is also presented in the paper. Evaluation of quality of VR/AR/MR platforms/environments should be based on (a) applying both expert-centred (top-down) and user-centred (bottom-up) quality evaluation methods and (b) separating ‘internal quality’ criteria, and ‘quality in use’ criteria in the set of quality criteria (model). Personalisation of VR/AR/MR platforms/environments should be based on learners’ models/profiles using students’ learning styles, intelligent technologies, and Semantic Web applications.     

Impact of information display on worker performance for wood frame wall assembly using AR HMD under different task conditions

Augmented Reality (AR) head-mounted displays (HMDs) provide users with an immersive virtual experience in the real world. The portability of this technology affords various information display options for construction workers that are not possible otherwise. However, the impact of these different information presentation options on human performance should be carefully evaluated before such technology is deployed in the jobsite. In this paper, we describe a research effort examining how different information displays presented via AR HMD influence task performance when assembling three sized wooden wall frame assembly tasks. We asked 18 construction engineering students with framing experience to finish three wood frame assembly tasks (large, medium, and small) using one of the three information displays (AR 3D conformal, AR 2D tag-along, and paper blueprints). The task performance was measured by time of completion and framing errors, which were analyzed and compared among each factor.     

Asymptotic analysis of temporal-difference learning algorithms with constant step-sizes

The mean-square asymptotic behavior of temporal-difference learning algorithms with constant step-sizes and linear function approximation is analyzed in this paper. The analysis is carried out for the case of discounted cost function associated with a Markov chain with a finite dimensional state-space. Under mild conditions, an upper bound for the asymptotic mean-square error of these algorithms is determined as a function of the step-size. Moreover, under the same assumptions, it is also shown that this bound is linear in the step size. The main results of the paper are illustrated with examples related to M/G/1 queues and nonlinear AR models with Markov switching. Editor: Robert Schapire     

Semi-formal transformation of secure business processes into analysis class and use case models: An MDA approach

ContextModel-Driven Development (MDD) is an alternative approach for information systems development. The basic underlying concept of this approach is the definition of abstract models that can be transformed to obtain models near implementation. One fairly widespread proposal in this sphere is that of Model Driven Architecture (MDA). Business process models are abstract models which additionally contain key information about the tasks that are being carried out to achieve the company’s goals, and two notations currently exist for modelling business processes: the Unified Modelling Language (UML), through activity diagrams, and the Business Process Modelling Notation (BPMN).ObjectiveOur research is particularly focused on security requirements, in such a way that security is modelled along with the other aspects that are included in a business process. To this end, in earlier works we have defined a metamodel called secure business process (SBP), which may assist in the process of developing software as a source of highly valuable requirements (including very abstract security requirements), which are transformed into models with a lower abstraction level, such as analysis class diagrams and use case diagrams through the approach presented in this paper.MethodWe have defined all the transformation rules necessary to obtain analysis class diagrams and use case diagrams from SBP, and refined them through the characteristic iterative process of the action-research method.ResultsWe have obtained a set of rules and a checklist that make it possible to automatically obtain a set of UML analysis classes and use cases, starting from SBP models. Our approach has additionally been applied in a real environment in the area of the payment of electrical energy consumption.ConclusionsThe application of our proposal shows that our semi-automatic process can be used to obtain a set of useful artifacts for software development processes.     

Large margin mixture of AR models for time series classification

In this paper, we propose the large margin autoregressive (LMAR) model for classification of time series patterns. The parameters of the generative AR models for different classes are estimated using the margin of the boundaries of AR models as the optimization criterion. Models that use a mixture of AR (MAR) models are considered for representing the data that cannot be adequately represented using a single AR model for a class. Based on a mixture model representing each class, we propose the large margin mixture of AR (LMMAR) models. The proposed methods are applied on the simulated time series data, electrocardiogram data, speech data for E-set in English alphabet and electroencephalogram time series data. Performance of the proposed methods is compared with that of support vector machine (SVM) based classifier that uses AR coefficients based features. The proposed methods give a better classification performance compared to the SVM based classifier. Being generative models, the LMAR and LMMAR models provide a generative interpretation that enables utilization of the rejection option in the high risk classification tasks. The proposed methods can also be used for detection of novel time series data.     

Real-time performance modelling of a Sustained Attention to Response Task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participants' reaction times during a monotonous task. A laboratory-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Relevant parameters are then used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models is compared to detect in real time – minute by minute – the lapses in vigilance during the task. It is shown that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables the detection of vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared with neural networks and generalised linear mixed models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks.

Statement of Relevance: Existing research on monotony is largely entangled with endogenous factors such as sleep deprivation, fatigue and circadian rhythm. This paper uses a Bayesian model to assess the effects of a monotonous task on vigilance in real time. It is shown that the negative effects of monotony on the ability to sustain attention can be mathematically modelled and predicted in real time using surrogate measures, such as reaction times. This allows the modelling of vigilance fluctuations.     

A UML profile for the conceptual modelling of structurally complex data: Easing human effort in the KDD process

ContextDomains where data have a complex structure requiring new approaches for knowledge discovery from data are on the increase. In such domains, the information related to each object under analysis may be composed of a very broad set of interrelated data instead of being represented by a simple attribute table. This further complicates their analysis.ObjectiveIt is becoming more and more necessary to model data before analysis in order to assure that they are properly understood, stored and later processed. On this ground, we have proposed a UML extension that is able to represent any set of structurally complex hierarchically ordered data. Conceptually modelled data are human comprehensible and constitute the starting point for automating other data analysis tasks, such as comparing items or generating reference models.MethodThe proposed notation has been applied to structurally complex data from the stabilometry field. Stabilometry is a medical discipline concerned with human balance. We have organized the model data through an implementation based on XML syntax.ResultsWe have applied data mining techniques to the resulting structured data for knowledge discovery. The sound results of modelling a domain with such complex and wide-ranging data confirm the utility of the approach.ConclusionThe conceptual modelling and the analysis of non-conventional data are important challenges. We have proposed a UML profile that has been tested on data from a medical domain, obtaining very satisfactory results. The notation is useful for understanding domain data and automating knowledge discovery tasks.     

The Revised Strain Index: an improved upper extremity exposure assessment model

Abstract

The Revised Strain Index (RSI) is a distal upper extremity (DUE) physical exposure assessment model based on: intensity of exertion, frequency of exertion, duration per exertion, hand/wrist posture and duration of task per day. The RSI improves upon the 1995 Strain Index (SI) by using continuous rather than categorical multipliers, and replacing duty cycle with duration per exertion. In a simulation of 13,944 tasks, the RSI and 1995 SI showed good agreement in risk predictions for 1995 SI scores ≤3 (safe) and >13.5 (hazardous). For tasks with 1995 SI scores of >3 and ≤13.5, the two models showed marked disagreement, with the RSI providing much greater discriminations between ‘safe’ and ‘hazardous’ tasks for various combinations of force, repetition and duty cycle. We believe the RSI is a substantially improved model that will be useful for DUE task analysis, intervention and design.

Practitioner Summary: RSI is a substantial improvement over the 1995 SI. It should be a valuable tool for designing and analysing tasks to determine risk of musculoskeletal injuries. RSI is applicable to a wide variety of tasks including very low force and very high repetition tasks such as keyboard use.     

Variation at work: alternations between physically and mentally demanding tasks in blue-collar occupations

Abstract

The aims of this questionnaire study were to describe the occurrence and desired number of alternations between mental and physical tasks in industrial and non-industrial blue-collar work, and determine to which extent selected personal and occupational factors influence these conditions. On average, the 122 participating workers (55 females) reported to have close to four alternations per day between mental and physical tasks, and to desire more alternations than they actually had. They also expressed a general preference for performing a physical task after a mental task and vice versa. In univariate regression models, the desired change in task alternations was significantly associated with gender, age, occupation, years with current work tasks and perceived job control, while occupation was the only significant determinant in a multiple regression model including all factors. Our results suggest that alternations between productive physical and mental tasks could be a viable option in future job rotation.

Practitioner Summary: We addressed attitudes among blue-collar workers to alternations between physically and mentally demanding tasks. More alternations were desired than those occurring in the job, and workers preferred performing a physical task after a mental and vice versa. Alternating physical and mental tasks could, thus, be a viable option in job rotation.     

Feasibility of augmented reality technology for communication in the construction industry

Problem statementThe current shortage of skilled and experienced management personnel in the construction industry makes it necessary to devise solutions that fully leverage the available expertise. Augmented Reality (AR) is an emerging technology that has the potential to provide contextually relevant communication to field engineers and offer value through real-time communication of spatiotemporal details.PurposeThis paper identifies the communication scenarios in which AR may (or may not) be able to replace in-person meetings to address construction-related communication needs, based on data collected from current practitioners. Furthermore, the feedback from practitioners defines contextual factors that would impact their willingness to adopt AR for communication in the domain of construction engineering and management.MethodologySemi-structured interviews were conducted with practitioners and thematic analysis was performed on the interview data. This process identified trends in the practitioners’ responses to reveal their perceptions on use of AR for communication and also the factors that may impact the success of AR for communication in practice.FindingsPractitioners perceived AR to be of value in the following situations: improving the communication between the design team and construction team; effectively leveraging the expertise of experienced personnel; and to aid in inspections. The factors affecting the adoption of this technology were classified into technological factors (e.g. Cost of equipment) and non-technological factors (e.g. Relationship building).ContributionMany researchers have explored AR for construction, but the vast majority have explored the topic in controlled settings. Conversely, this study relates to real-life practical settings with direct involvement of industry practitioners. Therefore, the contribution of this work is in defining the factors that must be considered if researchers or practitioners intend to realize the theoretical value suggested for AR in practical settings, while taking into account existing challenges.     

Verification of models for ballistic movement time and endpoint variability

A hand control movement is composed of several ballistic movements. The time required in performing a ballistic movement and its endpoint variability are two important properties in developing movement models. The purpose of this study was to test potential models for predicting these two properties. Twelve participants conducted ballistic movements of specific amplitudes using a drawing tablet. The measured data of movement time and endpoint variability were then used to verify the models. This study was successful with Hoffmann and Gan's movement time model (Hoffmann, 1981; Gan and Hoffmann 1988) predicting more than 90.7% data variance for 84 individual measurements. A new theoretically developed ballistic movement variability model, proved to be better than Howarth, Beggs, and Bowden's (1971) model, predicting on average 84.8% of stopping-variable error and 88.3% of aiming-variable errors. These two validated models will help build solid theoretical movement models and evaluate input devices.

Practitioner summary: This article provides better models for predicting end accuracy and movement time of ballistic movements that are desirable in rapid aiming tasks, such as keying in numbers on a smart phone. The models allow better design of aiming tasks, for example button sizes on mobile phones for different user populations.     

Compliance in service-oriented architectures: A model-driven and view-based approach

ContextEnsuring software systems conforming to multiple sources of relevant policies, laws, and regulations is significant because the consequences of infringement can be serious. Unfortunately, this goal is hardly achievable due to the divergence and frequent changes of compliance sources and the differences in perception and expertise of the involved stakeholders. In the long run, these issues lead to problems regarding complexity, understandability, maintainability, and reusability of compliance concerns.ObjectiveIn this article, we present a model-driven and view-based approach for addressing problems related to compliance concerns.MethodCompliance concerns are represented using separate view models. This is achieved using domain-specific languages (DSLs) that enable non-technical and technical experts to formulate only the excerpts of the system according to their expertise and domain knowledge. The compliance implementations, reports, and documentation can be automatically generated from the models. The applicability of our approach has been validated using an industrial case study.ResultsOur approach supports stakeholders in dealing with the divergence of multiple compliance sources. The compliance controls and relevant reports and documentation are generated from the models and hence become traceable, understandable, and reusable. Because the generated artifacts are associated with the models, the compliance information won’t be lost as the system evolves. DSLs and view models convey compliance concerns to each stakeholder in a view that is most appropriate for his/her current work task.ConclusionsOur approach lays a solid foundation for ensuring conformance to relevant laws and regulations. This approach, on the one hand, aims at addressing the variety of expertise and domain knowledge of stakeholders. On the other hand, it also aims at ensuring the explicit links between compliance sources and the corresponding implementations, reports, and documents for conducting many important tasks such as root cause analysis, auditing, and governance.     

Standardizing the narrative of use cases: A controlled vocabulary of web user tasks

ContextFor user interfaces design, the use of appropriate terminology in writing use case narratives may determine the effectiveness of the design process, facilitating communication within multidisciplinary web development teams and leading to agreed designs.ObjectiveThis paper proposes a user task vocabulary for web user interface design. This vocabulary compiles terms related to the use of web applications, regardless of the application domain, as a way of standardizing the terminology used in the elaboration of use case narrative. The use of the vocabulary would help to reduce misunderstandings within multidisciplinary web development teams.MethodThe construction of the vocabulary is based on the combination of both committee and empirical approaches. Committee approaches rely on experts in designing web applications, while empirical ones are focused on analyzing content objects, such as interaction design patterns and use case narratives.ResultsThe final version of the monolingual controlled vocabulary of web user tasks compiles a total of 40 tasks; each of them has a key term and a definition of the interaction carried out by users. Additionally, 41 semantic relationships were collected and represented as synonyms. The vocabulary has been assessed through an expert evaluation, proving its correctness and completeness, and an usability evaluation checking the efficacy of the vocabulary.ConclusionThe language employed in use case narratives can determine the value of use cases for designing user interfaces. The usage of a controlled vocabulary may allow designers to elaborate unambiguous use case narratives as a way of creating consistent task models for designing web user interfaces.     

An optimal estimation technique for increasing the accuracy of crop forecasts by combining remotely sensed and conventional forecast results.

Abstract

An optimal estimation (OE) technique has been used to increase the accuracy of crop acreage and yield estimates by combining results from remotely sensed (RS) data and conventional models. For crop acreage estimation the OE increased the accuracy of wheat acreage estimation when the first forecasts of the Directorate of Economics and Statistics (DES) were combined with state level RS estimates over the states of Haryana and Punjab in India.

To increase the accuracy of wheat yield forecasts an autoregressive (AR) model was developed. Results of AR model were optimally combined with RS-based estimates for Hisar and Karnal districts in Haryana, India. The OE results for a total of eight forecasts had a lower mean absolute per cent deviation than the forecasts using RS and AR approaches. The power of OE was further demonstrated by combining weather-based wheat yield model results for the state of Punjab (India) with first order AR model results, suggesting an increase in accuracy of forecasts by optimally combining results from two or more algorithms.     

Identification of physically demanding tasks performed during bushfire suppression by Australian rural firefighters

PurposeTo identify and characterize the physically demanding tasks undertaken during multi-day wildfire (known as bushfire in Australia) suppression by Australian rural firefighters.MethodsDuring semi-structured group interviews, thirty-one experienced male firefighters reviewed 53 firefighting tasks that could be performed during tanker-based bushfire suppression. Participants were asked to nominate the most physically demanding tasks and then define their typical frequencies, durations, operational importance and the dominant actions and activity types in each task.ResultsSeven tasks were identified as physically demanding. They were further categorized into three hose and four handtool (e.g., rakehoe) related activities. These tasks were assessed as moderately important to critical and were thought to occur less than one up to 700 times in a four-month bushfire ‘season’. Each task’s duration was estimated to last approximately 2–30 min depending on the task. Dominant actions were carry, drag, dig/rake actions in seven, three and four of the demanding tasks, respectively. ‘Strength-endurance’ was the dominant activity type for five of the seven tasks.ConclusionSeven fireground tasks, three using a hose and four using handtools were classified as physically demanding by incumbent firefighters. The combination of hose and handtool work indicates that the tanker-based bushfire suppression tactics used by Australian rural firefighters appears to be a hybrid of structural and wildfire firefighting techniques and may require a dedicated physiological analyses before the job demands for these firefighters can be quantified.