This special section on "Engineering Agent-Based Social Simulations" aims to represent the current state of the art in using Software Engineering (SE) methods in ABSS. It includes a mixture of theoretically oriented papers that describe frameworks, notations and methods adapted from SE and practice-oriented papers that demonstrate the application of SE methods in real world ABSS projects.

A method for assessing potential synergies among different sets of transport telematic services (TTSs) is suggested. An Intelligent Transport System enhances transport by delivering one or more TTSs. The ability to deliver multiple TTSs to address a wide range of stakeholder needs is gaining momentum, not only from a marketing perspective but also from a technological perspective. The total cost of TTSs can be reduced if they share functionalities (i.e., sub-services provided by telematic systems). We show how this synergy can be assessed with the help of clustering methods. Knowledge about possible synergies of functionalities is useful in the (re)design and eventual deployment of TTSs, especially when the underlying telematic systems are able to support multiple TTSs. To adapt the clustering method for this purpose, we suggest a mathematical formulation of synergy among functionalities of TTSs. By applying the method to a set of 32 TTSs, we obtain a cluster formation of these TTSs according to their synergy measures. Overall, the results suggest that the joint implementation of TTSs targeted toward some problem domains can lead to significant cost savings, for example, Road User Charging, Infrastructure Repair and Maintenance, and Information on the Transport of extra large goods for the management of road transport infrastructure.

Achieving security in Internet of Things environments has been identified as one of the top barriers for realizing the vision of smart, energy-efficient homes and buildings. In this context, understanding the risks related to the use and potential misuse of information about customers, partners, and end-users, as well as, forming methods for integrating security-enhancing measures in the design is not straightforward and thus requires substantial investigation. A risk analysis applied on a smart home automation system developed in a research project involving leading industrial actors has been conducted. The results indicate that with the implementation of standard security features, new as well as, current risks can be minimized to acceptable levels albeit that the most serious risks, i.e., those derived from the human factor, need more careful consideration, as they are inherently complex in nature.

Macro-level models is the dominating type of freight transport analysis models for supporting the decision-making in public authorities. Recently, also agent-based models have been used for this purpose. These two model types have complementing characteristics: macro-level models enable to study large geographic regions in low level of detail, whereas agent-based models enable to study entities in high level of detail, but typically in smaller regions. In this paper, we suggest and discuss three approaches for combining macro-level and agent-based modeling: exchanging data between models, conducting supplementary sub-studies, and integrating macro-level and agent-based modeling. We partly evaluate these approaches using two case studies and by elaborating on existing freight transport analysis approaches based on executing models in sequence.

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.

In most software development companies the road mapping and requirements prioritization process is a complex process in which product management experiences difficulties in getting timely and accurate customer feedback. The feedback loop from customers is slow and often there is a lack of mechanisms that allow for efficient customer data collection and analysis. As a result, there is the risk that requirements prioritization becomes opinion-based rather than data-driven, and that R&D investments are made without an accurate way of continuously validating whether they correspond to customer needs. We call this phenomenon the 'open loop' problem, referring to the challenges for product management to get accurate and timely feedback from customers. To address this problem, we develop the HYPEX model (Hypothesis Experiment Data-Driven Development) that supports companies in running feature experiments to shorten customer feedback loops. We evaluate the model in three software development companies and observe how feature experiments increase the opportunity for data-driven software development.

Purpose: To investigate the possibilities, risks and requirements of a dynamic shelf life service – a technological innovation focusing on minimizing food waste in supply chains (SC). Design/methodology/approach: Semi-structured, open-ended interviews with SC actors have been used to identify the requirements, possibilities and risks with a dynamic shelf life service. Field tests have been conducted to investigate practical implications and effects of small temperature variations on shelf life. The field tests involved sensors based on Bluetooth Low Energy (BLE) and Radio Frequency Identification (RFID). Findings: The results show that a DSLP service holds great potentials. The field tests revealed that shelf life predictions are sensitive to small temperature differences along the cold chain. Results from the interviews confirm the importance of accuracy. The interviews also emphasize the importance of sharing costs among the involved actors. Research limitations/implications: Key aspects from 11 SC actors concerning dynamic shelf life prediction in cold chains are provided. The field tests involve a SC from production to household. Practical implications: Implementation of a dynamic shelf life service can increase visibility and information flow within SCs. The system can also be integrated with companies’ business systems creating new business opportunities and reducing manual work by automatically alerting quality fluctuations of food products. Original/value: Quantitative and qualitative data from 11 SC actors are provided. This information, together with the experiences reported from the field tests, have the potential to help replacing dysfunctional date labelling systems and reduce food waste.

In the context of globalization, the requirements on transport logistics continuously rise. Often goods travel through many different countries, using several transport modes and involving a number of different actors. Implementing some level of intelligence on the goods, which provide them with the capabilities to assist in the logistical activities, is one of the instruments that can be used to improve control and efficiency in transports and goods-handling. The concept of intelligent goods both opens up for new types of services and may be used to improve currently available services. The research is mainly focused on the characteristics, possible architectures, and applications of intelligent goods systems. In this context, an intelligent goods system refers to a number of interacting components, e.g. on-board units, servers, and RFID tags, which together provide intelligent goods services. Intelligent goods refer to goods with a higher degree of intelligence than just providing the ID of the goods, and generally the concept involve information processing and/or storage on or close to the goods, acting on behalf of the goods throughout the whole transport. The purpose of the studies is to investigate how intelligent goods can be used to improve goods transports in terms of more efficient goods-handling as well as better control of the goods and the transportation process, but also in terms of more efficient information sharing, e.g. between different actors. This may in turn provide reduced costs, environmental impact and usage of infrastructure. The research is concentrated on the communication and processing of information before, during and after transport. Most of the research results are applicable to ii goods transport by any mode, whereas some of the research has an emphasis on road transport. A framework is presented which can be used to describe intelligent goods systems, including the capabilities of the goods, necessary information entities related to the goods, as well as a number of primitive services that can be used as building blocks when creating more advanced intelligent goods services. Furthermore, a new approach to service description is proposed, which can be used to, amongst others, define an intelligent goods service and to perform architecture analyses. By identifying architectures corresponding to different service solutions, intelligent goods can be compared with other types of solutions, for instance more centralized approaches. In particular, different situations and services put different requirements on a system and the benefits of using intelligent goods vary. In order to investigate how intelligent goods may be applied in practice, two services have been examined in more detail: a dynamic shelf- life prediction service, and a consignment-level emission allocation service. These studies involve field tests, interviews and simulations. Finally, an investigation of how intelligent goods systems can be modelled as multi-agent systems is also included.

Today, it is common to include machine learning components in software products. These components offer specific functionalities such as image recognition, time series analysis, and forecasting but may not satisfy the non-functional constraints of the software products. It is difficult to identify suitable learning algorithms for a particular task and software product because the non-functional requirements of the product affect algorithm suitability. A particular suitability evaluation may thus require the assessment of multiple criteria to analyse trade-offs between functional and non-functional requirements. For this purpose, we present a method for APPlication-Oriented Validation and Evaluation (APPrOVE). This method comprises four sequential steps that address the stated evaluation problem. The method provides a common ground for different stakeholders and enables a multi-expert and multi-criteria evaluation of machine learning algorithms prior to inclusion in software products. Essentially, the problem addressed in this article concerns how to choose the appropriate machine learning component for a particular software product.