Designinig Coordination among Human and Software Agents

The goal of this paper is to propose a new methodology for designing coordination between human angents 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

Theory of Regulatory Compliance for Requirements Engineering

Regulatory compliance is increasingly being addressed in the practice of requirements engineering as a main stream concern. This paper points out a gap in the theoretical foundations of regulatory compliance, and presents a theory that states (i) what it means for requirements to be compliant, (ii) the compliance problem, i.e., the problem that the engineer should resolve in order to verify whether requirements are compliant, and (iii) testable hypotheses (predictions) about how compliance of requirements is verified. The theory is instantiated by presenting a requirements engineering framework that implements its principles, and is exemplified on a real-world case study.Comment: 16 page

A situational approach for the definition and tailoring of a data-driven software evolution method

Successful software evolution heavily depends on the selection of the right features to be included in the next release. Such selection is difficult, and companies often report bad experiences about user acceptance. To overcome this challenge, there is an increasing number of approaches that propose intensive use of data to drive evolution. This trend has motivated the SUPERSEDE method, which proposes the collection and analysis of user feedback and monitoring data as the baseline to elicit and prioritize requirements, which are then used to plan the next release. However, every company may be interested in tailoring this method depending on factors like project size, scope, etc. In order to provide a systematic approach, we propose the use of Situational Method Engineering to describe SUPERSEDE and guide its tailoring to a particular context.Peer ReviewedPostprint (author's final draft

Using intentional analysis to model knowledge management requirements in communities of practice

This working document presents a Knowledge Management (KM) fictitious scenario to be modeled using Intentional Analysis in order to guide us on choosing the appropriate Information System support for the given situation. In this scenario, a newcomer in a knowledge organization decides to join an existing Community of Practice (CoP) in order to share knowledge and adjust to his new working environment. The preliminary idea suggests that Tropos is used for the Intentional Analysis, allowing us to elicit the requirements for a KM system, followed by the use of Agent-Object-Relationship Modeling Language (AORML) on the architectural and detailed design phases of software development. Aside of this primary goal, we also intend to point out needs of extending the expressiveness of the current Intentional analysis modeling language we are using and to check where the methodology could be improved in order to make it more usable. This is the first version of this working document, which we aim to constantly update with our new findings resulting of progress in the analysis

Mining and searching app reviews for requirements engineering: Evaluation and replication studies

App reviews provide a rich source of feature-related information that can support requirement engineering activities. Analysing 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