Modern code review (MCR) is a practice in which code reviews are performed in a tool-supported, asynchronous and lightweight way. Previous studies show that MCR can lead to improved internal and external outcomes. Differently from many previous works, our aim is to provide insights regarding MCR practices from a developer-centric perspective in a mid-sized organization . We surveyed developers of a mid-sized Brazilian software company, using a survey instrument that both included validated items from the literature and novel items specific to our survey goals. We collected N = 73 responses in an initial survey phase and N = 29 responses in a follow-up phase. Our results provide insights in various dimensions regarding the opinion of developers on how an MCR process should be configured to be effective in an organization. In terms of managerial implications, our survey sheds light on the extrinsic and intrinsic motivations of developers to engage in MCR activities. The success of MCR depends both on having an appropriately configured process as well as the on support of managers in terms of making MCR benefits visible to stakeholders and appreciating review efforts. • We study developer perceptions of modern code review processes. • We identify which configuration of the process developers consider optimal to work. • Acknowledgment by other developers is found to be a key motivating factor. • The use of informal communication can lead to knowledge vaporization.

Background: Collaboration among software engineers through face-to-face discussions in teams has been promoted since the adoption of agile methods. However, these discussions might demote the contribution of software engineers who are introverts, possibly leading to sub-optimal solutions and creating work environments that benefit extroverts. Objective: We aim to evaluate whether providing software engineers with time to work individually and reason about a collective problem is a setting that makes introverts more comfortable to interact and contribute more, ultimately leading to better solutions. Method: We plan to conduct a between-subjects study, with teams in a control group that design a software architecture in a team discussion meeting and teams in a treatment group in which subjects work individually before engaging in a meeting. We will assess and compare the amount of contribution of introverts, their subjective experiences, and the designed solutions. Limitations: As extroverts will be present in both groups, we will not be able to conclude that better solutions are solely due to the increased participation of introverts. The analyses of their subjective experience and amount of contributions might provide evidence to suggest the reasons for observed differences.

Network function virtualisation (NFV) decouples network functions from the underlying hardware by means of virtualisation, thus enabling the replacement of proprietary middleboxes by software components that can be dynamically deployed and configured on demand. The selection of the most appropriate virtual network functions (VNFs) to achieve a particular objective, and the decision on where to deploy these VNFs and through which paths they will communicate, are the responsibilities of an NFV orchestrator. In this paper, we propose to orchestrate VNFs using interacting cognitive components structured with the BDI architecture, leading to emergent solutions to address network challenges. More specifically, we extend a previously proposed reverse auction protocol and propose a novel bidding heuristic that can be used to make decisions regarding the orchestration tasks. We validate our model in a DDoS attack case study, in which we demonstrate that our components can successfully mitigate the attack.

Psychological theory distinguishes between maximizing and satisficing decision-making styles. Maximizers tend to explore more or all alternatives when making a choice, while satisficers evaluate options until they find one that is good enough. There is limited research that examines how the existence of a recommender influences the choice process and decisions of different types of decision-makers. We report the results of a controlled study, in which we monitored the choice process of participants when provided with automated recommendations and different types of additional information regarding available options. Our analyses show that none of the differences that were expected based on the literature manifested itself in the experiment. Maximizers neither inspected more items, nor invested more time to study them. Instead, like satisficers, they mostly picked one of the top-ranked items recommended by the system, which emphasizes the value of recommenders in particular for maximizers, who would otherwise face a more challenging decision problem. The analysis of the preferences of participants over different types of additional information revealed that highlighting key pros and cons was perceived as particularly helpful for the maximizers, an insight that can be used for the design of explanation approaches for recommenders.

Summary Meeting performance and scalability requirements while delivering services is a critical issue in web applications. Recently, latency and cost of Internet‐based services are encouraging the use of application‐level caching to continue satisfying users' demands and improve the scalability and availability of origin servers. Application‐level caching, in which developers manually control cached content, has been adopted when traditional forms of caching are insufficient to meet such requirements. Despite its popularity, this level of caching is typically addressed in an ad hoc way, given that it depends on specific details of the application. Furthermore, it forces application developers to reason about a crosscutting concern, which is unrelated to the application business logic. As a result, application‐level caching is a time‐consuming and error‐prone task, becoming a common source of bugs. Among all the issues involved with application‐level caching, the decision of what should be cached must frequently be adjusted to cope with the application evolution and usage, making it a challenging task. In this paper, we introduce an automated caching approach to automatically identify application‐level cache content at runtime by monitoring system execution and adaptively managing caching decisions. Our approach is implemented as a framework that can be seamlessly integrated into new and existing web applications. In addition to the reduction of the effort required from developers to develop a caching solution, an empirical evaluation showed that our approach significantly speeds up and improves hit ratios with improvements ranging from 2.78% to 17.18%.

Domain-specific languages are a means of speeding up and easing software system development. Agent-based simulations are simulation systems in which autonomous entities, namely agents, are developed in order to reproduce, analyze, or predict a phenomenon under study. Despite its potential, available alternatives for agent-based modeling and simulation benefit from recurrent domain-related concepts, such as domain concerns and standard strategies for creating and initializing entities, in a limited way. Such elements, when represented in models, can potentially ease the modeling and simulation. We propose a domain-specific modeling language that provides ready-to-use concepts for these recurrent agent-based simulation elements with a modularized notation.The language is targeted to the agent-based modeling and simulation domain and is currently focused on modeling the simulated environment. A user study provides evidence that our language decreases the time needed to understand agent-based simulations.

Pervasive computing allows users to benefit from information and services provided by advanced applications using smart and interconnected devices. Given the inherent complexity of this software environment, platforms become a typical means of easing the development and execution of pervasive applications. These platforms are usually in charge of managing the application context, ensuring that data are reliable and available to applications. Most platforms provide this feature, making real-time information available, even when applications do not need such information in real-time. This occurs, for example, when sensors continuously provide measurements to in-house temperature controllers and these measurements have limited time variance. Therefore, controllers can be updated only occasionally, reducing application overload without compromising effectiveness. To address this issue, in this paper, we present a pervasive platform, iCasa, extended with an autonomic manager of service-oriented context module. Our solution consists of tracking application needs and context information to achieve the best caching configuration, regarding a multi-application pervasive environment. This caching configuration can decrease the power consumption of devices due to the reduced unnecessary network communications between platform and devices.

Network function virtualisation (NFV) decouples network functions from physical devices, simplifying the deployment of new services. As opposed to traditional middleboxes, VNFs can be dynamically deployed and reconfigured on demand, posing strict management challenges to networked systems. Selecting VNFs from a repository, defining where they will be placed in the virtualised network as well as chaining them to achieve the desired behaviour are problems that have to be tackled by an orchestrator. In this paper, we propose a distributed approach to NFV orchestration using belief-desire-intention (BDI) reasoning, addressing the selection, placement and chaining problems through the interaction among autonomous software agents, which collectively work in a distributed and decentralised manner. Agents are capable of bidding on the allocation of resources for new VNFs, as well as managing the chaining of VNFs. Further, we validate our theoretical model through a DDoS attack case study, in which we analyse the emergent behaviour of the autonomous agents.

Latency and cost of Internet-based services are encouraging the use of application-level caching to continue satisfying users' demands, and improve the scalability and availability of origin servers. Despite its popularity, this level of caching involves the manual implementation by developers and is typically addressed in an ad-hoc way, given that it depends on specific details of the application. As a result, application-level caching is a time-consuming and error-prone task, becoming a common source of bugs. Furthermore, it forces application developers to reason about a crosscutting concern, which is unrelated to the application business logic. In this paper, we present the results of a qualitative study of how developers handle caching logic in their web applications, which involved the investigation of ten software projects with different characteristics. The study we designed is based on comparative and interactive principles of grounded theory, and the analysis of our data allowed us to extract and understand how developers address cache-related concerns to improve performance and scalability of their web applications. Based on our analysis, we derived guidelines and patterns, which guide developers while designing, implementing and maintaining application-level caching, thus supporting developers in this challenging task that is crucial for enterprise web applications.

Preferences have been widely studied in several areas including computer science, as they play an important role in many computational tasks, such as decision making support, providing recommendations and personalizing applications. Although many approaches consider particular preference representation models to be used as input for algorithms to address these tasks, there is a need for identifying a model that provides adequate constructions for users to express their preferences. In this paper, we propose a preference meta-model that provides various preference constructs, which include end-user expressions. We start by describing an exploratory study of how people express their preferences in natural language, which provides the basis for the meta-model. After describing the meta-model, we evaluate it with two user studies in different domains. The results of this evaluation indicate that the preference statements that can be expressed with the meta-model are adequate for allowing users to indicate their preferences to a computational system.

Explanations play an essential role in decision support and recommender systems as they are directly associated with the acceptance of those systems and the choices they make. Although approaches have been proposed to explain automated decisions based on multi-attribute decision models, there is a lack of evidence that they produce the explanations users need. In response, in this paper we propose an explanation generation technique, which follows user-derived explanation patterns. It receives as input a multi-attribute decision model, which is used together with user-centric principles to make a decision to which an explanation is generated. The technique includes algorithms that select relevant attributes and produce an explanation that justifies an automated choice. An evaluation with a user study demonstrates the effectiveness of our approach.

Understanding how a software system is structured, i.e. its architecture, is crucial for software comprehension. It allows developers to understand an implemented system and reason about how non-functional requirements are addressed. Yet, many systems lack any architectural documentation, or it is often outdated due to software evolution. In current practice, the process of recovering a system's architecture relies primarily on developer knowledge. Although existing architecture recovery approaches can help to identify architectural elements, these approaches require improvement to identify architectural concepts of a system automatically. Towards this goal, we analyze the usefulness of adopting different code-level characteristics to group elements into architectural modules. Our main contributions are an evaluation of the relationships between different sets of characteristics and their corresponding accuracies, and the evaluation results, which help us to understand which characteristics reveal information about the source code structure. Our experiment shows that an identified set of characteristics achieves an average accuracy of 80%, which indicates the usefulness of the considered characteristics for architecture recovery and thus to improving software comprehension.

Feature model configuration is known to be a hard, error-prone and time-consuming activity. This activity gets even more complicated when it involves multiple stakeholders in the configuration process. Research work has proposed approaches to aid multi-stakeholder feature model configuration, but they rely on systematic processes that constraint decisions of some of the stakeholders. In this paper, we propose a novel approach to improve the multi-stakeholder configuration process, considering stakeholders' preferences expressed through both hard and soft constraints. Based on such preferences, we recommend different product configurations using different strategies from the social choice theory. We conducted an empirical study to evaluate the effectiveness of our strategies with respect to individual stakeholder satisfaction and fairness among all stakeholders. Results indicate that particular strategies perform best with respect to these aspects.

Twenty-first century global change is challenging our use and management of all resources. For a community to adapt and yet maintain and even enhance its economy and quality of life, there is a need for collaborative innovation (CI) and related action among concerned members of the community. This paper outlines the concepts of an approach to CI based on dynamic asset-mapping and its support through a web-based technological framework. Based on real-world experience with the framework, it is clear that CI takes many forms and that it is not possible to build a single set of tools to support CI. Rather a framework and a set of meta-tools is needed which can be used to build tailored systems to fit specific situations that arise when collaboration is to occur.