Franci Suni-Lopez

Bio: Franci Suni-Lopez is an academic researcher. The author has contributed to research in topics: Instrumentation (computer programming) & Conceptual model. The author has an hindex of 1, co-authored 5 publications receiving 2 citations.

Understanding Implicit User Feedback from Multisensorial and Physiological Data: A case study

Abstract: Ensuring the quality of user experience is very important for increasing the acceptance likelihood of software applications, which can be affected by several contextual factors that continuously change over time (e.g., emotional state of end-user). Due to these changes in the context, software continually needs to adapt for delivering software services that can satisfy user needs. However, to achieve this adaptation, it is important to gather and understand the user feedback. In this paper, we mainly investigate whether physiological data can be considered and used as a form of implicit user feedback. To this end, we conducted a case study involving a tourist traveling abroad, who used a wearable device for monitoring his physiological data, and a smartphone with a mobile app for reminding him to take his medication on time during four days. Through the case study, we were able to identify some factors and activities as emotional triggers, which were used for understanding the user context. Our results highlight the importance of having a context analyzer, which can help the system to determine whether the detected stress could be considered as actionable and consequently as implicit user feedback.

Towards an Automatic Generation of Persuasive Messages

Abstract: In the last decades, the Natural Language Generation (NLG) methods have been improved to generate text automatically. However, based on the literature review, there are not works on generating text for persuading people. In this paper, we propose to use the SentiGAN framework to generate messages that are classified into levels of persuasiveness. And, we run an experiment using the Microtext dataset for the training phase. Our preliminary results show 0.78 of novelty on average, and 0.57 of diversity in the generated messages.

How Do Negative Emotions Influence on the Conceptual Models Verification?: A live study proposal

Abstract: The present live study is proposed with the objective of investigating the influence of negative emotions (i.e., stress) in the efficiency for verifying conceptual models. To conduct this study, we use a Model-driven Testing tool, named CoSTest, and our own version of stress detector within a competition setting. The experiment design, overview of the empirical procedure, instrumentation and potential threats are presented in the proposal.

How Can Personality Influence Perception on Security of Context-Aware Applications?

Abstract: [Context and Motivation] Our lives are being transformed by context-aware software applications with important social, environmental, and economic implications. [Question/Problem] Experts recognized that quality attributes, e.g. security, are the cornerstone to get healthy social implications of these applications. However, do end-users (service consumers) perceive these attributes as so important? [Methodology] To answer this question, we designed a survey, to understand how end-users perceive security of context-aware software applications and how the users’ personality traits might influence their perceptions. To this end, we did a web-based survey that embeds two animated-demonstration videos in order to present i) the functionality of a context-aware mobile app, and ii) some vulnerabilities of the mobile app. It involved 48 subjects divided in two groups: subjects with software engineering (SE) background (Group A) and subjects without any SE background (Group B). [Results] Our study found that the importance of confidentiality and integrity is more clearly perceived by subjects with SE backgrounds (Group A). Accountability is more difficult to be perceived by subjects. And this difficulty can be even more pronounced for subjects without any SE background (Group B). Our findings suggest that importance preferences on security are influenced by personality types. For instance, open-minded people have a higher propensity to perceive the importance of confidentiality and integrity. Whilst, people with a high level of agreeableness hold quite different perceptions regarding the importance of authenticity and accountability. Analyzing the level of association between personality and the perceived importance on security, we found that the importance perceptions on confidentiality are influenced by the personality of subjects from Group B. And, the changes (positive an negative) in the importance perception on confidentiality are very strongly influenced by personality, even more so by the personality of subjects from Group B.

Analyzing Emotions in Conceptual Models Verification Tasks performed in Online Contests

Abstract: Emotion research in the area of software engineering has gained significant attention. Mostly this research has been focused on understanding the role of emotions in software programming carried out within collaborative software development environments. With the purpose of providing more evidence on emotion research in the early stages of the software life cycle, in this paper, we report the results of a live study conducted in competitive conditions. The main objective of the study is to analyze the emotions expressed by competitors when performing verification tasks with the support of CoSTest, a model-driven testing tool. Our results show that participants tend to experience more positive emotions (e.g., attentive, alert, active) than negative emotions (upset, hostile, afraid) when verification tasks are performed in an online contest.

Specifying requirements for collection and analysis of online user feedback

Abstract: According to data-driven Requirements Engineering (RE), explicit and implicit user feedback can be considered a relevant source of requirements, thus supporting requirements elicitation. However, limited attention has been paid so far to the role of online feedback in RE tasks, such as requirements validation, and on how to specify what online feedback to collect and analyse. We performed an action research study, together with a company that developed a platform for online training. This paper presents the design and execution of the study, and a discussion of its results. This study provides evidence about the need of practitioners to follow a simple but systematic approach for specifying requirements for data collection and analysis, at design time. Another outcome of this study is a method to tackle this task that leverages goal-oriented requirements modelling combined with Goal-Question-Metric. The applicability of the method has been explored on two industrial evaluations, while the perceived effectiveness, efficiency and acceptance have been assessed with practitioners through a dedicated survey.

Social Engineering Attacks and Defenses in the Physical World vs. Cyberspace: A Contrast Study

Abstract: Social engineering attacks are phenomena that are equally applicable to both the physical world and cyberspace. These attacks in the physical world have been studied for a much longer time than their counterpart in cyberspace. This motivates us to investigate how social engineering attacks in the physical world and cyberspace relate to each other, including their common characteristics and unique features. For this purpose, we propose a methodology to unify social engineering attacks and defenses in the physical world and cyberspace into a single framework, including: (i) a systematic model based on psychological principles for describing these attacks; (ii) a systematization of these attacks; and (iii) a systematization of defenses against them. Our study leads to several insights, which shed light on future research directions towards adequately defending against social engineering attacks in cyberspace.

Understanding User Feedback Through Negative Emotions: A Learning Experience

Abstract: User feedback is an important topic to be taught in Software Engineering (SE) courses. Furthermore, it includes theoretical concepts related to questionnaires, the time elapsed, mouse movements, etc., that are used to understand the topic better. In this context, teaching and learning theoretical concepts in different courses are great challenges in education in universities, particularly in this topic, due to the lack of practical applications or examples in real-life problems. Therefore, it is important to adapt the theoretical concepts to the advancement of technology. In this paper, we propose an innovative methodology for teaching implicit user feedback through the analysis of actionable emotions (i.e., emotions triggered by the interaction with a software service). We apply our proposal during three sessions; in the first and second, students reviewed the theoretical concepts in class. The last one was carried out in the ICE-InnovaT studio, where a system recognizes negative emotions in real-time from a user who was interacting with a software service; in this session, the students were watching the results of the system during the user interaction. Our experience indicates positive results in the adoption of this new approach. Overall, students reported positive comments related to using emotion recognition technologies to understand implicit user feedback.

Persuasive Dialogue Corpus: Graph-Based Approach Combining Persuader and Persuadee Perspectives

Abstract: AbstractPersuasion is omnipresent in our daily communication. As a mechanism for changing or forming one’s opinion or behavior, persuasive dialogues and their strategies have gained interest for developing intelligent conversational systems. Given the complexity of this task, persuasion systems, especially dealing in conversations that require ‘no action’ by the user but rather a change in opinion or belief, require specialized annotated corpora and the understanding of logical structure, natural language, and persuasive strategies. The sparsity of available annotated data and a wide range of proposed models make it challenging for developing strategic chatbots specific to user needs. To address these issues, this study introduces a novel framework combining a replicable data collection tool and a topic-independent annotation schema for designing an argument-graph corpus and incorporating both persuader and persuadee perspectives, essential for building smart conversational agents. KeywordsPersuasionConversational agentAnnotation schemaNeo4jGraph-based corpusChatbot

Analyzing Emotions in Conceptual Models Verification Tasks performed in Online Contests

Abstract: Emotion research in the area of software engineering has gained significant attention. Mostly this research has been focused on understanding the role of emotions in software programming carried out within collaborative software development environments. With the purpose of providing more evidence on emotion research in the early stages of the software life cycle, in this paper, we report the results of a live study conducted in competitive conditions. The main objective of the study is to analyze the emotions expressed by competitors when performing verification tasks with the support of CoSTest, a model-driven testing tool. Our results show that participants tend to experience more positive emotions (e.g., attentive, alert, active) than negative emotions (upset, hostile, afraid) when verification tasks are performed in an online contest.