Visual GUI testing (VGT) is an emerging technique that provides software companies with the capability to automate previously time-consuming, tedious, and fault prone manual system and acceptance tests. Previous work on VGT has shown that the technique is industrially applicable, but has not addressed the real-world applicability of the technique when used by practitioners on industrial grade systems. This paper presents a case study performed during an industrial project with the goal to transition from manual to automated system testing using VGT. Results of the study show that the VGT transition was successful and that VGT could be applied in the industrial context when performed by practitioners but that there were several problems that first had to be solved, e.g. testing of a distributed system, tool volatility. These problems and solutions have been presented together with qualitative, and quantitative, data about the benefits of the technique compared to manual testing, e.g. greatly improved execution speed, feasible transition and maintenance costs, improved bug finding ability. The study thereby provides valuable, and previously missing, contributions about VGT to both practitioners and researchers.

System- and acceptance-testing are primarily performed with manual practices in current software industry. However, these practices have several issues, e.g. they are tedious, error prone and time consuming with costs up towards 40 percent of the total development cost. Automated test techniques have been proposed as a solution to mitigate these issues, but they generally approach testing from a lower level of system abstraction, leaving a gap for a flexible, high system-level test automation technique/tool. In this paper we present JAutomate, a Visual GUI Testing (VGT) tool that fills this gap by combining image recognition with record and replay functionality for high system-level test automation performed through the system under test's graphical user interface. We present the tool, its benefits compared to other similar techniques and manual testing. In addition, we compare JAutomate with two other VGT tools based on their static properties. Finally, we present the results from a survey with industrial practitioners that identifies test-related problems that industry is currently facing and discuss how JAutomate can solve or mitigate these problems.

In this paper, we propose a method for supporting the process of designing Intelligent Transportation System (ITS) services, which utilizes on primarily functional synergies with already existing services. Using synergies between services will enable sharing of resources, such as, information entities, functions and technical resources, which in turn may lead to reduced costs for implementing services. The method is built around an existing service description framework, which is used to describe both existing services and the service to be designed. In order to illustrate the usage of the suggested method, we have applied it for designing a new ITS service, i.e., the Liability Intelligent Transport System (LITS) service. The purpose of the LITS service is to support the process of identifying when, where and why freight has been damaged, and which actor was responsible when the freight was damaged. The LITS service may lead to better quality control of consignments and may also facilitate the identification of which actor was responsible for the freight damage, which is of particular interest in multi-modal transport. By applying our service design method we were able to identify that three out of four functions of the LITS service already exist in other existing ITS services. Therefore, the LITS service can be designed based on synergies with these services.

We present a study on information synergy between an electronic waybill (e-Waybill) and intelligent transportation system (ITS) services. A waybill is an important transport document, and it contains essential information about a consignment. We consider five e-Waybill solutions, which differ in where the e-Waybill information is stored, read, and written. We analyse 20 ITS services, and for each of them, the required input entities that can be provided by an e-Waybill are identified, and the synergy with each of the e-Waybill solutions is determined based on the location of the e-Waybill information. The analysis shows that e-Waybill solutions with storage at the freight-level enable the highest information synergy with ITS services. Our result may support the implementation of practical e-Waybill systems that provide high synergy with ITS services, which may lead to higher utilisation of ITS services and more sustainable transport, e.g., in terms of reduced congestion and emissions

Software startups are more popular than ever and growing in numbers. They operate under conditions of extreme uncertainty and face many challenges. Often, agile development practices and lean principles are suggested as ways to increase the odds of succeeding as a startup, as they both advocate close customer collaboration and short feedback cycles focusing on delivering direct customer value. However, based on an interview study we see that despite guidance and support in terms of well-known and documented development methods, practitioners find it difficult to implement and apply these in practice. To explore this further, and to propose operational support for software startup companies, this study aims at investigating (1) what are the typical challenges when finding a product idea worth scaling, and (2) what solution would serve to address these challenges. To this end, we propose the ‘Early Stage Software Startup Development Model’ (ESSSDM). The model extends already existing lean principles, but offers novel support for practitioners for investigating multiple product ideas in parallel, for determining when to move forward with a product idea, and for deciding when to abandon a product idea. The model was evaluated in a software startup project, as well as with industry professionals within the software startup domain.

We present Open Trumps, a version of the popular card game Top Trumps with decks that are procedurally generated based on open data. The game is played among multiple players through drawing cards and selecting the feature that is most likely to trump the same feature on the other players’ cards. Players can generate their own decks through choosing a suitable dataset and setting certain attributes; the generator then generates a balanced and playable deck using evolutionary computation. In the example dataset, each card represents a country and the features represent such entities as GDP per capita, mortality rate or tomato production, but in principle any dataset organised as instances with numerical features could be used. We also report the results of an evaluation intended to investigate both player experience and the hypothesis that players learn about the data underlying the deck they play with, since understanding the data is key to playing well. The results show that players enjoy playing the game, are enthusiastic about its potential and answer questions related to decks they have played significantly better than questions related to decks they have not played.

When individual teams in mechatronic organizations attempt to adopt agile software practices, these practices tend to only affect mod- ules or sub-systems. The short iterations on team level do not lead to short lead-times in launching new or updated products since the overall R&D approach on an organization level is still governed by an overall stage gate or single cycle V-model. This paper identifies challenges for future research on how to combine the predictability and planning desired of mechanical manufacturing with the dynamic capabilities of modern agile software development. Scaling agile in this context requires an expansion in two dimensions: First, scal- ing the number of involved teams. Second, traversing necessary systems engineering activities in each sprint due to the co-dependency of software and hardware development.

In our connected world, recommender services have become widely known for their ability to provide expert and personalize information to participants of diverse applications. The excessive growth of social networks, a new kind of services are being embraced which are termed as "group based recommendation services", where recommender services can be utilized to discover sub-communities within implicit social groups and provide referrals to new participants in order to join various sub-communities of other participants who share similar preferences or interests. Nevertheless, protecting participants' privacy in recommendation services is a quite crucial aspect which might prevent participants from exchanging their own data with these services, which in turn detain the accuracy of the generated referrals. So in order to gain accurate referrals, recommendation services should have the ability to discover previously unknown sub-communities from different social groups in a way to preserve privacy of participants in each group. In this paper, we present a middleware that runs on end-users' mobile phones to sanitize their profiles' data when released for generating referrals, such that computation of referrals continues over the sanitized version of their profiles' data. The proposed middleware is equipped with cryptography protocols to facilitate private discovery of sub-communities from the sanitized version of participants' profiles in a university scenario. Location data are added to participants' profiles to improve the awareness of surrounding sub-communities, so the offered referrals can be filtered based on adjacent locations for participant's location. We performed a number of different experiments to test the efficiency and accuracy of our protocols. We also developed a formal model for the tradeoff between privacy level and accuracy of referrals. As supported by the experiments, the sub-communities were correctly identified with good accuracy and an acceptable privacy level.

Modern healthcare's need for knowledge sharing and bridging the research–practice gap requires new forms of collaboration, in which clinicians of varying clinical and research expertise work together over geographical and organisational borders. To support such distributed communities of practice (CoPs), an understanding of their collaboration processes, outcomes, challenges and enablers is needed. The article examines these issues through a case study of a long-running CoP, the Swedish Oral Medicine Network (SOMNet). SOMNet's main form of collaboration is monthly telephone conference meetings centred on case consultations. Cases are submitted by the clinicians via a Web-based system. The methods used were interviews, observations, and a questionnaire. The work adds to previous research by studying a distributed CoP explicitly focused on supporting the transfer of scientific results from researchers to practitioners. We found that the regular meetings give a rhythm to the community. The centrality of cases means an immediate benefit for the submitter while the community is provided an authentic context for learning. SOMNet yields opportunities for help and learning for diverse expertise levels; the type of benefits is affected by the participant's degree of oral medicine knowledge and collaboration involvement. There are challenges in accommodating varying levels of expertise and encouraging those less experienced to participate. Enablers of the collaboration include the participation of experts, meeting facilitators and well-adapted ITs.

With increasing amounts of open data, especially where data can be connected with various additional information resources, new ways of visualizing and making sense of this data become possible and necessary. This paper proposes, discusses and exemplifies the concept of data games, games that allow the player(s) to explore data that is derived from outside the game, by transforming the data into something that can be played with. The transformation takes the form of procedural content generation based on real-world data. As an example of a data game, we describe Open Data Monopoly, a game board generator that uses economic and social indicator data for local governments in the UK. Game boards are generated by first collecting user input on which indicators to use and how to weigh them, as well as what criteria should be used for street selection. Sets of streets are then evolved that maximize the selected criteria, and ordered according to “prosperity” as defined subjectively by the user. Chance and community cards are created based on auxiliary data about the local political entities.

Prevention and detection of plagiarism has formed the basis of much research, but student perceptions on plagiarism are arguably not well understood. This is particularly the case in the computing disciplines. This paper considers two aspects of the student experience: (i) the types of plagiaristic activity that students engage in, and (ii) the specific understanding of what plagiarism means for students who write computer programmes. In a recent study, data were collected from published material (books, published papers, websites), and online formative quizzes and questionnaires used by universities to test student knowledge of what constitutes plagiarism. Facet analysis was used to classify the data into four initial categories (sources, actions, material, extrinsic). Further analysis suggested a refinement to six categories and 23 sub-categories which directly relate to the computing disciplines. In a further study a large-scale online questionnaire was carried out to obtain the perceptions of students on source-code plagiarism. Data were collected from 770 students studying at 21 higher education institutions in the UK and overseas. This study’s results suggest that certain types of plagiaristic activity are poorly understood. This paper summarises and compares the results of these two studies and reflects on the implications for educating computing students about how they should avoid plagiarism.

Ubiquitous computing technologies have the potential to revolutionize the support of chronic health conditions: improving quality of life, reducing costs and optimizing health outcomes. Wearable networks of connected devices and sensors offer the prospect of personalized support and contextually aware advice, for those with specific chronic health conditions. However, there are many obstacles and concerns that need to be addressed before the full potential can be realized. This workshop aims to bring together those interested in developing ubiquitous health management and related personal decision support systems to identify how gaps in knowledge can be addressed and design practices can be improved to better support key communities and contexts of use in this rapidly growing field.

The basic prerequisite for methodological advance in Multi-Agent Based Modelling and Simulation is a clear, ideally formally-grounded, concept of our subject. A commonly accepted, implementation-independent meta-model may improve the status of MABS as a scientific field providing a solid foundation that can be used for describing, comparing, analysing, and understanding MABS models. In this contribution, we present an attempt formalizing a general view of MABS models by defining the AMASON meta-model that captures the basic structure and dynamics of a MABS model.

A straightforward natural iterative heuristic for correlation clustering in the general setting is to start from singleton clusters and whenever merging two clusters improves the current quality score merge them into a single cluster. We analyze the approximation and complexity aspects of this heuristic and its randomized variant where two clusters to merge are chosen uniformly at random among cluster pairs amenable to merge.

This study evaluated a pilot workshop for teaching communication skills to dental students. The methodology is based on an experiential learning approach, the use of realistic clinical scenarios, simulated patients, and an integrated teaching team of both educational researchers and dentists. Furthermore, the methodology was adapted for short workshops, which is thought to offer better possibilities for frequent and effective training of communication skills throughout the curriculum. The workshop was piloted with groups of six to ten students from the sixth and tenth semesters (n=94). Results show that the majority of students found the tasks meaningful and well aligned with how they perceived their future profession as dentists. Most students also thought that they learned from the task. An interesting finding is that students not only found it instructive to practice how to communicate in authentic situations, but that they generally found the workshop to be thought-provoking while at the same time providing structure and intellectual tools for the future. A possible explanation for this finding is the sharing of explicit criteria for high-quality communication.

Intelligent Truck Parking (ITP) involves efficient management of information as a tool for addressing some of the problems related to truck parking, such as, the inability to navigate to, or reserve a parking space. Different telematic services (ITP core services) may be required to address different stakeholder needs, e.g., a navigation service for finding the way to a truck parking area, and a reservation service for assuring the availability of a parking space when in the parking area. In addition to ITP core services, there are several other telematic services that address transport problems in general. If the synergies of different services are taken into account during design and deployment of telematic services, cost reduction may be achieved through functionality sharing. In this article, we have identified services that are relevant for sharing functionalities with ITP core services, estimate functionality costs and perform a synergy analysis to assess the marginal cost savings as a result. Overall results indicates that Traffic Information Broadcasting (TIB), Estimated Time of Arrival (ETA) for goods, and Geo-fencing (GEO) can form clusters with ITP core services that could lead to better cost savings compared to the rest of the services studied.

Large projects are increasingly adopting agile development practices, and this raises new challenges for research and practice. The fourth workshop on large-scale agile development focused on the following topics: Distributed large-scale development, inter-team coordination, knowledge sharing, large-scale agile transformations, multidisciplinary work, and new ways-of-organizing for advancing agile practices.

The two-watchman route problem is that of computing a pair of closed tours in an environment so that the two tours together see the whole environment and some length measure on the two tours is minimized. Two standard measures are: the minmax measure, where we want the tours where the longest of them has minimal length, and the minsum measure, where we want the tours for which the sum of their lengths is smallest. It is known that computing the minmax two-watchman route is NP-hard for simple rectilinear polygons and thus also for simple polygons. We exhibit a polynomial time 7.1416-factor approximation algorithm for computing the minmax two-watchman route in simple polygons.

'Internet of Things' (IoT) systems are fundamentally changing the way in which we interact and perceive technology. In this paper, we focus on two dimensions of IoT systems; (1) the IoT user interface and (2) the IoT ecosystem. We develop a model that identifies how data is presented to users and how users interact with the system, and the level at which systems interconnect with, and collects data from, external systems. Companies can use the model to map their systems according to the dimensions in order to: (1) identify current state of their systems, (2) identify desired state and (3) better understand the steps necessary to develop more advanced IoT systems. We evaluate the dimensions in five case companies and provide empirical evidence on the transition towards increasingly advanced IoT systems.

Most companies today experience a situation in which they are part of a complex business ecosystem of stakeholders that influence business outcomes. Especially for companies transitioning from selling products to becoming systems, solutions and services providers, this is causing a significant shift in their business strategies and relationships. Instead of focusing on internal processes, companies need to strategically position themselves in a dynamic network of actors to accelerate synergies and value co-creation. However, while this shift in business strategy is inevitable, it is not without challenges. An understanding for how to align internal, as well as external processes is critical, as well as a careful assessment on how to establish strategic partnerships in a dynamic network of interests. Based on on-going research, this paper outlines the emerging challenges that most software development companies face when adopting an ecosystem-driven approach, and the different mitigation strategies to manage these.

With recent and rapid advances in areas such as online games, embedded systems and Internet of Things,the traditional notion of what constitutes a system is fundamentally changing.Similarly to Systems of Systems (SoS) these systems are heterogeneous, autonomous and allow dynamic and emergent configurations that evolve and adjust over time. Also, these systems allow automated optimization of system performance.Regarded as the new digital business paradigm, these types of systems offer fundamentally new ways for software development companies in their service-and value creation. At the same time, they present challenges in these organizations. In this paper, and based on multiple case study research in three different domains, we identify emergent system characteristics that pose new challenges on software development and we outline the transition towards new ways-of-working in software development.

While innovation, such as development of new features, is critical for any organization, it is hard to get right. In both our case companies, the selection of ideas is usually driven by previous experiences, and very often the process becomes politicized and based on peoples’ opinions. To address this, we present the Hypothesis Experiment Data-Driven Development (HYPEX) model. Our model is an alternative development process that helps companies shorten the feedback loop to customers. The model supports companies in running feature experiments and advocates development of small parts of features that are continuously evaluated with customers. In our study we validate the model in two software development companies. Although the companies involved in the study have not yet completed a full experiment cycle, we see that feature experiments are beneficial for improving at least four activities within the companies: (1) data-driven development (the ease of collecting customer feedback allows for a real-time connection between the quantified business goals of the organization and the operational metrics collected from the installed customer base), (2) customer responsiveness (the ease of collecting customer feedback allows product management to respond rapidly and dynamically to any changes to the use of the products, as well as to emerging customer requests), (3) R&D efficiency (the ease of collecting customer feedback gives the development teams a real-time goal and metrics to strive for and provides focus for their work), and (4) R&D accuracy (the ease of collecting customer feedback enables the development teams to align their efforts with what the customers appreciate the most). The HYPEX model is a development process that helps software development companies move away from building large chunks of functionality with little feedback from customers and instead continuously validate with customers that the functionality under development is of value to customers.

During the last decade, the vast majority of software companies have adopted agile development practices. Now companies are looking to move beyond agile and further advance their practices. In this paper, we report on the experiences of a company in the embedded systems domain that is adopting agile practices with the intention to move beyond agile and towards continuous deployment of software. Based on case study research involving group interviews and a web-based survey, we identify challenges in relation to (1) the adoption of agile practices, (2) testing practices, (3) continuous deployment, and (4) customer validation.

While close customer collaboration is highlighted as a distinguishing characteristic in agile development, difficulties arise in large-scale agile development where the product owner can no longer represent the different needs of a large customer base. While most companies use the role of a product owner to represent the customer base, experiences show that prioritizations that are made are far from optimal. Also, once the decision to develop a feature has been taken, companies stop to continuously validate if this feature adds value to the large customer base. As experienced in the case companies we work with, re-prioritization of feature content is difficult once development has started, resulting in R&D investments in development of features that have no proven customer value. In this paper, and based on our experiences from working with five B2B software development companies, we present a conceptual model in which qualitative and quantitative customer feedback techniques allow for continuous validation and re-prioritization of feature content. In this way, large-scale software development companies can significantly improve responsiveness to customers throughout the development cycle, while at the same time increase accuracy of their development efforts.

Today, products within telecommunication, transportation, consumer electronics, home automation, security etc. involve an increasing amount of software. As a result, organizations that have a tradition within hardware development are transforming to become software-intensive organizations. This implies products where software constitutes the majority of functionality, costs, future investments, and potential. While this shift poses a number of challenges, it brings with it opportunities as well. One of these opportunities is to collect product data in order to learn about product use, to inform product management decisions, and for improving already deployed products. In this paper, we focus on the opportunity to use post-deployment data, i.e. data that is generated while products are used, as a basis for product improvement and new product development. We do so by studying three software development companies involved in large-scale development of embedded software. In our study, we highlight limitations in post-deployment data usage and we conclude that post-deployment data remains an untapped resource for most companies. The contribution of the paper is two-fold. First, we present key opportunities for more effective product development based on post-deployment data usage. Second, we propose a framework for organizations interested in advancing their use of post-deployment product data.

This paper explores the integration of social networks with crowdsourcing games for generating archival metadata. We studied crowdsourcing, gamification and social dynamics from the perspective of cultural heritage and combined their features in a metadata game prototype on the Facebook platform. We tested our prototype and evaluated its results by analysing participation, contribution and player feedback. The two-week testing phase showed promising results in terms of user engagement and produced metadata. We conclude that deploying metadata games on social net- working platforms is a feasible method for digital archives to harness human intelligence from large shared spaces.

The Internet of Things phenomena is reflected in the increase in the types of devices that can be joined to form computing ecosystems, the growth of the amount and variety of data collected and processed, and the power and reach of applications that can be created on these platforms. While these new applications provide potentially valuable new capabilities to individuals, organizations, and society, they also entail significant risks. This paper presents the VALUES framework to identify potential impacts that should be addressed when designing Internet of Things (IoT) applications. For each of the six areas, questions and issues that should be considered are discussed.

In the workplace, IOT technologies were initially used in a manner analogous to their use in smart homes, controlling lighting, heating and cooling and monitoring energy usage. Location-based sensors, including RFID, are used to track the movements of employees, and can interact with the lighting, heating and cooling controls. Traditional electronic performance monitoring data (computer use, audio and video monitoring) can be used in conjunction with other data captured by IOT devices. IOT technologies are being tested for use in training (McGowan, 2015), injury prevention (Kortuem et al., 2010), promoting cohesion (Kirkham et al., 2013), space utilization and employee interactions (Mathur et al., 2015a) and security and surveillance (Miorandi et al., 2012). The consumerization of workplace technology (Harris et al., 2012), wherein employees bring their personal smart phones and health monitors into the workplace, has made it very easy to monitor employee's locations and activities. Already in 2011, over 40% of the devices used to access business applications were the users’ personally owned devices (Gens, et al., 2011). The driver of this trend is the workers themselves, possibly because increased accessibility gives workers a sense of autonomy and flexibility that outweighs the downside of increased work demands outside of work hours (Cavazotte et al., 2014). In some organizations such as BP and Autodesk, fitness tracking devices are provided to employees as part of the corporate wellness program (Nield, 2014). These programs focus on helping people to prevent illnesses or improve health through their behavior, and may also monitor stress levels to manage mental health (Mirarchi et al., 2015). “The potential economic benefits to an organization such as reduced absenteeism, increased productivity, increased stress tolerance and improved decision-making, as well as the physical and mental health benefits for employees, means that there is a strong business case for using the workplace as a vehicle for health promotion efforts of this kind” (Kries & Brodeker, 2004, as cited in McEachan, et al., 2011, p.1) While the use of IOT technologies in the workplace may appear benign, concerns have been raised regarding the potential for abuse. Having one's location and personal interactions and communications tracked by a Quantified Workplace system may be considered a breach of both work-environment privacy and solitude privacy as defined by Ball et al. (2012). Monitoring and routinization of work processes may have a negative impact on employee work life (Carter et al., 2011). “At a minimum, we can speak of declining welfare for workers and the associated regime of total mobilisation and surveillance corrode workers’ health and safety, creating anxiety, burnout and overwork” (Moore & Robinson, 2015, p. 8). The accessibility of data from multiple sources, such as GPS, email, social media and personal devices, allows employers to combine data on behavior at work with other information, search for patterns, and draw conclusions which reflect information that the employee chose not to share her employer (Holtgrewe, 2014).

This qualitative study investigates challenges associated with technical dependencies and their communication. Such challenges frequently occur when agile practices are scaled to large-scale software development. The use of thematic analysis on semi-structured interviews revealed five challenges: planning, task prioritization, knowledge sharing, code quality, and integration. More importantly, these challenges interact with one another and can lead to a domino effect or vicious circle. If an organization struggles with one challenge, it is likely that the other challenges become problematic as well. This situation can have a significant impact on process and product quality. Our recommendations focus on improving planning and knowledge sharing (with practices such as scrum-of-scrums, continuous integration, open space technology) to break the vicious circle, and to reestablish effective communication across teams, which will then enable large-scale companies to achieve the benefits of large-scale agility.

Reusable learning objects support packaging of educational materials allowing their discovery and reuse. Open educational resources emphasize the need for open licensing and promote sharing and community involvement. For both teachers and learners, finding appropriate tried and tested resources on a topic of interest and being able to incorporate them within or alongside other learning materials can enrich provision and share best practice. Resources are made available by a number of general and subject-specific repositories, but there are also many educational resources residing outside these repositories which may provide useful additional materials. Potential users of materials need to be able to locate relevant material and to assess it with respect to a number of factors (such as suitability for purpose and license requirements). However, even such basic requirements can be less than straightforward to determine. This paper presents a view of the field from the user's perspective, bringing together themes from existing research relating to practice-oriented concerns including discoverability, reusability, and quality. It provides a background in this area, exploring current trends, controversies, and research findings. The discussion is also aligned with current provision and practice, indicating areas where further research, provision, and support would be useful.

We describe Bar Chart Ball, a game where players indirectly control a ball by modifying a bar chart that the ball rests on. The bar chart displays real-world demographic data about the UK, and the player modifies the chart by selecting which aspect of the data to focus on. By making data selection a core game mechanic, in fact the only game mechanic, we advance a novel and simple way of building game content from data, and of making data visualisation playable.

The purpose of this paper is to show the technical feasibility of implementing our mobile context-aware learning schedule (mCALS) framework as a software application on a mobile device using current technologies, prior to its actual implementation. This process draws together a set of compatible mobile and context-aware technologies at present and can be used as a reference point for implementing generic mobile context-aware applications. Our mCALS framework retrieves the learner’s location and available time contexts via the built-in learning schedule (i.e. electronic organizer) on a mobile device. These contexts together with the learner’s learning styles and knowledge level (on a selected topic) are used as the basis for the software application to suggest learning materials that are appropriate for the learner, at the time of usage. This retrieval approach attempts to eliminate the use of context-aware technologies and the need to directly request the user to enter context information at the time of usage. We aim to develop a fully functional prototype of this framework for learners to plan their individual as well as social learning activities amongst one another in order to make their individual learning processes collaborative and as a way to enhance individual and social learning experiences.