arXiv subjects

Elena Masserini

Publications and source records attributed to Elena Masserini.

7 recordsLinked to original sources

arXiv2026

Automated Testing of Task-based Chatbots: How Far Are We?

Task-based chatbots are software, typically embedded in real-world applications, that assist users in completing tasks through a conversational interface. As chatbots are gaining popularity, effectively assessing their quality has become crucial. Whereas traditional testing techniques fail to systematically exercise the conversational space of chatbots, several approaches specifically targeting chatbots have emerged from both industry and research. Although these techniques have shown advancements over the years, they still exhibit limitations, such as simplicity of the generated test scenarios and weakness in implemented oracles. In this paper, we conduct a confirmatory study to investigate such limitations by evaluating the effectiveness of state-of-the-art chatbot testing techniques on a curated selection of task-based chatbots from GitHub, developed using the most popular commercial and open-source platforms.

cs.SE↗

arXiv2026

Multi-Level Testing of Conversational AI Systems

Conversational AI systems combine AI-based solutions with the flexibility of conversational interfaces. However, most existing testing solutions do not straightforwardly adapt to the characteristics of conversational interaction or to the behavior of AI components. To address this limitation, this Ph.D. thesis investigates a new family of testing approaches for conversational AI systems, focusing on the validation of their constituent elements at different levels of granularity, from the integration between the language and the AI components, to individual conversational agents, up to multi-agent implementations of conversational AI systems

cs.SE↗

arXiv2026

Assessing Task-based Chatbots: Snapshot and Curated Datasets for Dialogflow

In recent years, chatbots have gained widespread adoption thanks to their ability to assist users at any time and across diverse domains. However, the lack of large-scale curated datasets limits research on their quality and reliability. This paper presents TOFU-D, a snapshot of 1,788 Dialogflow chatbots from GitHub, and COD, a curated subset of TOFU-D including 185 validated chatbots. The two datasets capture a wide range of domains, languages, and implementation patterns, offering a sound basis for empirical studies on chatbot quality and security. A preliminary assessment using the Botium testing framework and the Bandit static analyzer revealed gaps in test coverage and frequent security vulnerabilities in several chatbots, highlighting the need for systematic, multi-Platform research on chatbot quality and security.

cs.SE↗

arXiv2025

Towards Multi-Platform Mutation Testing of Task-based Chatbots

Chatbots, also known as conversational agents, have become ubiquitous, offering services for a multitude of domains. Unlike general-purpose chatbots, task-based chatbots are software designed to prioritize the completion of tasks of the domain they handle (e.g., flight booking). Given the growing popularity of chatbots, testing techniques that can generate full conversations as test cases have emerged. Still, thoroughly testing all the possible conversational scenarios implemented by a task-based chatbot is challenging, resulting in incorrect behaviors that may remain unnoticed. To address this challenge, we proposed MUTABOT, a mutation testing approach for injecting faults in conversations and producing faulty chatbots that emulate defects that may affect the conversational aspects. In this paper, we present our extension of MUTABOT to multiple platforms (Dialogflow and Rasa), and present experiments that show how mutation testing can be used to reveal weaknesses in test suites generated by the Botium state-of-the-art test generator.

cs.SE↗

arXiv2025

Bug Whispering: Towards Audio Bug Reporting

Bug reporting is a key feature of mobile applications, as it enables developers to collect information about faults that escaped testing and thus affected end-users. This paper explores the idea of allowing end-users to immediately report the problems that they experience by recording and submitting audio messages. Audio recording is simple to implement and has the potential to increase the number of bug reports that development teams can gather, thus potentially improving the rate at which bugs are identified and fixed. However, audio bug reports exhibit specific characteristics that challenge existing techniques for reproducing bugs. This paper discusses these challenges based on a preliminary experiment, and motivates further research on the collection and analysis of audio-based bug reports

cs.SE↗

arXiv2025

Towards the Assessment of Task-based Chatbots: From the TOFU-R Snapshot to the BRASATO Curated Dataset

Task-based chatbots are increasingly being used to deliver real services, yet assessing their reliability, security, and robustness remains underexplored, also due to the lack of large-scale, high-quality datasets. The emerging automated quality assessment techniques targeting chatbots often rely on limited pools of subjects, such as custom-made toy examples, or outdated, no longer available, or scarcely popular agents, complicating the evaluation of such techniques. In this paper, we present two datasets and the tool support necessary to create and maintain these datasets. The first dataset is RASA TASK-BASED CHATBOTS FROM GITHUB (TOFU-R), which is a snapshot of the Rasa chatbots available on GitHub, representing the state of the practice in open-source chatbot development with Rasa. The second dataset is BOT RASA COLLECTION (BRASATO), a curated selection of the most relevant chatbots for dialogue complexity, functional complexity, and utility, whose goal is to ease reproducibility and facilitate research on chatbot reliability.

cs.SE↗

arXiv2024

Anonymizing Test Data in Android: Does It Hurt?

Failure data collected from the field (e.g., failure traces, bug reports, and memory dumps) represent an invaluable source of information for developers who need to reproduce and analyze failures. Unfortunately, field data may include sensitive information and thus cannot be collected indiscriminately. Privacy-preserving techniques can address this problem anonymizing data and reducing the risk of disclosing personal information. However, collecting anonymized information may harm reproducibility, that is, the anonymized data may not allow the reproduction of a failure observed in the field. In this paper, we present an empirical investigation about the impact of privacy-preserving techniques on the reproducibility of failures. In particular, we study how five privacy-preserving techniques may impact reproducibilty for 19 bugs in 17 Android applications. Results provide insights on how to select and configure privacy-preserving techniques.

cs.SE↗