App reviews provide a rich source of feature-related information that can support requirement engineering activities. Analyzing them manually to find this information, however, is challenging due to their large quantity and noisy nature. To overcome the problem, automated approaches have been proposed for ‘feature-specific analysis’. Unfortunately, the effectiveness of these approaches has been evaluated using different methods and datasets. Replicating these studies to confirm their results and to provide benchmarks of different approaches is a challenging problem. We address the problem by extending previous evaluations and performing a comparison of these approaches. In this paper, we present two empirical studies. In the first study, we evaluate opinion mining approaches; the approaches extract features discussed in app reviews and identify their associated sentiments. In the second study, we evaluate approaches searching for feature-related reviews. The approaches search for users’ feedback pertinent to a particular feature. The results of both studies show these approaches achieve lower effectiveness than reported originally, and raise an important question about their practical use.

App reviews can provide valuable information about user needs but analyzing them manually is challenging due to their large quantity and noisy nature. To overcome this problem, a variety of app review mining techniques have been proposed. So far, however, research in this area has paid little attention to the software engineering use cases of the mining techniques. This limits the understanding of their usefulness, applications and desired future developments. We address this problem by elaborating a reference model relating app review mining techniques to specific software engineering activities. In this paper, we present a unified description of software engineering use cases for mining app reviews and define a reference architecture realizing these use cases through a combination of natural language processing and data mining techniques. The use cases provide a novel systematic exposition of the envisioned applications and benefits of app review mining for software engineers. The reference architecture synthesises the diversity of research to realise these benefits and provide a general framework guiding the development and evaluation of future research and tools.

Abstract App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.

Research on self-adaptive systems (SASs) has proliferated in the last fifteen years. Approaches resting on models at run-time have been proposed (e.g., to model system variants), as well as methods that aim at giving requirements a key role in driving the adaptation process (e.g., to choose the most appropriate system variant). More recent research focuses on automating model-based decisions, such as requirements revision, by exploiting data generated at execution time.Uncertainty is considered a first-class citizen in SAS engineering. A well recognised technique for dealing with uncertainty is risk management. Several risk management methods exist, as well as visual modelling languages that aim at supporting risk analysis.Our objective is to investigate how complementing requirements modelling with risk modelling could support automating risk-driven requirements analysis. While risk could be identified and modelled at design-time using domain knowledge and data generated by previous system executions, their estimation will be done at run-time, and guide the selection of system behaviour that minimises the risk of the system not being compliant with requirements.In this paper, we introduce our research objective that concerns the definition of an engineering framework, called Risk4SAS, that enables risk-driven requirements analysis in SASs life-cycle and describe first steps towards its realisation, including a meta-model, which captures the dependency between risk and the characteristics of a SAS’s variants. We conclude by presenting our research road-map.

Gamification is increasingly applied to engage people in performing tool-supported collaborative tasks. From previous experiences we learned that available gamification guidelines are not sufficient, and more importantly that motivational and acceptance aspects need to be considered when designing gamified software applications. To understand them, stakeholders need to be involved in the design process. This paper aims to (i) identify key requirements for designing gamified solutions, and (ii) understand if existing methods (partially fitting those requirements) can be selected and combined to provide a comprehensive gamification design method. We discuss a set of key requirements for a suitable gamification design method. We illustrate how to select and combine existing methods to define a design approach that fits those requirements usingDesign Thinking and the Agon framework. Furthermore, we present a first empirical evaluation of the integrated design method, with participants including both requirements analysts and end-users of the gamified software. Our evaluation offers initial ideas towards a more general, systematic approach for gamification design.

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Automated decision-making techniques are useful to support engineers when performing requirements engineering tasks. However, to be effectively used in practice they need to be integrated into the organisational context, in which stakeholder engagement becomes a critical adoption factor. In this paper, we propose a tool-supported collaborative requirements prioritisation process, called GRP, which exploits gamification elements to engage distributed stakeholders to contribute to the overall decision-making process. Analytic Hierarchy Process is used as key component of the game engine, and enables an iterative prioritisation process. The GRP process has been evaluated through an exploratory case study, which has been conducted at a small software company, providing us with preliminary evidence about the effectiveness of the proposed solution. The main findings and lessons learned from the case study are presented.

Automated decision-making techniques have been proposed to support engineers in selecting and prioritising requirements. However, to be effectively used in practice they need to be integrated into the organisational context, and their users, namely the members of the development team, and more generally the project's stakeholders, need to be engaged in the resulting tool-supported decision-making process. In this demo paper, we present a tool-supported collaborative requirements prioritisation process, which exploits game elements to engage distributed stakeholders to contribute to the overall decision-making process. AHP and Genetic Algorithms are used as key component of the game engine, which enables an iterative prioritisation process. The tool is part of the tool-suite developed in the SUPERSEDE project which aims at supporting a flexible feedback-anddata-driven software evolution approach.

Gamification has been applied in software engineering contexts, and more recently in requirements engineering with the purpose of improving the motivation and engagement of people performing specific engineering tasks. But often an objective evaluation that the resulting gamified tasks successfully meet the intended goal is missing. On the other hand, current practices in designing gamified processes seem to rest on a try, test and learn approach, rather than on first principles design methods. Thus empirical evaluation should play an even more important role.We combined gamification and automated reasoning techniques to support collaborative requirements prioritization in software evolution. A first prototype has been evaluated in the context of three industrial use cases. To further investigate the impact of specific game elements, namely point-based elements, we performed a quasi-experiment comparing two versions of the tool, with and without pointsification. We present the results from these two empirical evaluations, and discuss lessons learned.

As different types of user feedback are becoming available, from a variety of sources and in large amount, several analysis techniques have been developed with the purpose of extracting information that can be useful for requirements engineering purposes. For instance, automated extraction and prioritization of feature requests have been recently investigated for the specific case of app development, where the key prioritization criterion is value for the user. For other types of software applications and services, software evolution relies on multi-criteria requirements prioritization, which may take into account different stakeholders' perspectives, thus leading to a complex decision-making problem. Different automated reasoning techniques have been proposed to support multi-criteria requirements prioritization, aimed at reducing human effort and improving the quality of the resulting ranking of the candidate requirements.The goal of our research is to understand how we can exploit user feedback in tool-supported multi-criteria requirements prioritization processes. Towards this objective, we discuss the properties of user feedback which are relevant for requirements prioritization, formulate a multi-criteria requirements prioritization problem, and outline a possible solution that integrates state of the art automated reasoning techniques which we extend to cope with information derived from user feedback.

A relevant activity in the requirements engineering process consists in the identification, assessment and management of potential risks, which can prevent the system-to-be from meeting stakeholder needs. However, risk analysis techniques are often time- and resource- consuming activities, which may introduce in the requirements engineering process a significant overhead. To overcome this problem, we aim at supporting risk management activity in a semi-automated way, merging the capability to exploit existing risk-related information potentially present in a given organisation, with an automated ranking of the goals with respect to the level of risk the decision-maker estimates for them. In particular, this paper proposes an approach to address the general problem of risk decision-making, which combines knowledge about risks assessment techniques and Machine Learning to enable an active intervention of human evaluators in the decision process, learning from their feedback and integrating it with the organisational knowledge. The long term objective is that of improving the capacity of an organisation to be aware and to manage risks, by introducing new techniques in the field of risk management that are able to interactively and continuously extract useful knowledge from the organisation domain and from the decision-maker expertise.

The significant wide impact of mobile technologies (e.g., smartphones, tablets) and the difficulty of mastering their complexity (due to factors like constant emergence and evolution) pose new challenges to many (if not all) software engineering disciplines. We particularly see these challenges when thinking of average citizens that carry out their daily activities in smart environments where mobile technologies and sensors installed provide many potential advantages to support them. Applications that could enable informed decision-making are currently beyond what software developers can provide. This position paper discusses challenges, and highlights current approaches available in order to support decision-making for thoughtful living. We present an initial version of a comprehensive framework to overcome the challenges identified and analyse which software engineering research lines may help to implement it. A motivating scenario is used to conduct the discussion.

Deciding which, among a set of requirements, are to be considered first and in which order is a strategic process in software development. This task is commonly referred to as requirements prioritization. This paper describes a requirements prioritization method called Case-Based Ranking (CBRank), which combines project's stakeholders preferences with requirements ordering approximations computed through machine learning techniques, bringing promising advantages. First, the human effort to input preference information can be reduced, while preserving the accuracy of the final ranking estimates. Second, domain knowledge encoded as partial order relations defined over the requirement attributes can be exploited, thus supporting an adaptive elicitation process. The techniques CBRank rests on and the associated prioritization process are detailed. Empirical evaluations of properties of CBRank are performed on simulated data and compared with a state-of-the-art prioritization method, providing evidence of the method ability to support the management of the tradeoff between elicitation effort and ranking accuracy and to exploit domain knowledge. A case study on a real software project complements these experimental measurements. Finally, a positioning of CBRank with respect to state-of-the-art requirements prioritization methods is proposed, together with a discussion of benefits and limits of the method.

Service-Based Applications (SBA) are defined as software systems, which are composed of multiple services made available over computer networks by different service providers, and that can be accessed by service consumers, through a variety of connecting devices.

Service-Based Applications (SBA) are inherently open and distributed, as they rely on third-party services that are available over the Internet, and have to cope with the dynamism of such operating environment. This motivates the need for SBA to be self-adaptive to accommodate changes in service availability and performance, in consumers' needs and preferences, and more generally in the operational environment, which may occur at run-time. Engineering such applications significantly challenges the role of requirements engineering (RE). Usually, RE is carried out at the outset of the whole development process, but in the context of SBA, RE activities are also needed at run-time thus enabling a seamless SBA evolution. In this paper, we investigate RE for SBA at run-time proposing a method that supports the continuous refinement of requirements artifacts at run-time, which involves consumers and the SBA itself as primary stakeholders.

Self-adaptive systems should autonomously adapt at run time to changes in their operational environment, guided by the goals assigned by their stakeholders. We present a tool that supports goal-oriented modelling and generation of code for goal-directed, self-adaptive systems, supporting Tropos4AS, an extension of the software engineering methodology Tropos.

Many classes of distributed applications, including e-business, e-government, and ambient intelligence, consist of networking infrastructures, where the nodes (peers)—be they software components, human actors or organizational units—cooperate with each other to achieve shared goals. The multi-agent system metaphor fits very well such settings because it is founded on intentional and social concepts and mechanisms. Not surprisingly, many agent-oriented software development methods have been proposed, including GAIA, PASSI, and Tropos . This paper extends the Tropos methodology, enhancing its ability to support high variability design through the explicit modelling of alternatives, it adopts an extended notion of agent capability and proposes a refined Tropos design process. The paper also presents an implemented software development environment for Tropos , founded on the Model-Driven Architecture (MDA) framework and standards. The extended Tropos development process is illustrated through a case study involving an e-commerce application.

Current ICT application domains, such as Web services and autonomic computing, call for highly flexible systems, capable of adapting to changing operational environments as well as to user needs. Multi-agent system framework do include mechanisms that make flexibility and adaptability possible. In our research we focus on how to take into account environmental constraints and stakeholder needs in the design of software agent capabilities

Case-based driven approaches to requirements prioritization proved to be much more effective than first-principle methods in being tailored to a specific problem, that is they take advantage of the implicit knowledge that is available, given a problem representation. In these approaches, first-principle prioritization criteria are replaced by a pairwise preference elicitation process. Nevertheless case-based approaches, using the analytic hierarchy process (AHP) technique, become impractical when the size of the collection of requirements is greater than about twenty since the elicitation effort grows as the square of the number of requirements. We adopt a case-based framework for requirements prioritization, called case-based ranking, which exploits machine learning techniques to overcome the scalability problem. This method reduces the acquisition effort by combining human preference elicitation and automatic preference approximation. Our goal in this paper is to describe the framework in details and to present empirical evaluations which aim at showing its effectiveness in overcoming the scalability problem. The results prove that on average our approach outperforms AHP with respect to the trade-off between expert elicitation effort and the requirement prioritization accuracy.

The goal of this paper is to propose a new methodology for designing coordination between human agents and software agents and, ultimately, among software agents. The methodology is based on two key ideas. The first is that coordination should be designed in steps, according to a precise software engineering methodology, and starting from the specification of early requirements. The second is that coordination should be modeled as dependency between actors. Two actors may depend on one another because they want to achieve goals, acquire resources or execute a plan. The methodology used is based on Tropos, an agent oriented software engineering methodology presented in earlier papers. The methodology is presented with the help of a case study.

This paper addresses the problem of efficiently updating a network of temporal constraints when constraints are removed from or added to an existing network. Such processing tasks are important in many AI applications requiring a temporal reasoning module. First we analyze the relationship between shortest-paths algorithms for directed graphs and arc-consistency techniques. Then we focus on a subclass of STP for which we propose new fast incremental algorithms for consistency checking and for maintaining the feasible times of the temporal variables.