Fabio Palomba

• PostDoc Position at Delft University of Technology

Large Language Models (LLMs) have rapidly transformed software development, especially in code generation. However, their inconsistent performance, prone to hallucinations and quality issues, complicates program comprehension and hinders maintainability. Research indicates that prompt engineering-the practice of designing inputs to direct LLMs towa...

The advent of Large Language Models (LLMs) is transforming software development, significantly enhancing software engineering processes. Research has explored their role within development teams, focusing on specific tasks such as artifact generation, decision-making support, and information retrieval. Despite the growing body of work on LLMs in so...

Successful software development depends on effectively managing both collaboration and technology. However, socio-technical challenges can disrupt team dynamics and lead to technical debt. Despite the interdisciplinary nature of teams working on ML-enabled systems , research on their socio-technical dynamics remains limited compared to the emphasis...

As a socio-technical activity, software development involves the close interconnection of people and technology. The integration of Large Language Models (LLMs) into this process exemplifies the socio-technical nature of software development. Although LLMs influence the development process, software development remains fundamentally human-centric,...

As machine learning (ML) systems become central to critical decision-making, concerns over fairness and potential biases have increased. To address this, the software engineering (SE) field has introduced bias mitigation techniques aimed at enhancing fairness in ML models at various stages. Additionally, recent research suggests that standard ML en...

The exploit or the Proof of Concept of the vulnerability plays an important role in developing superior vulnerability repair techniques, as it can be used as an oracle to verify the correctness of the patches generated by the tools. However, the vulnerability exploits are often unavailable and require time and expert knowledge to craft. Obtaining t...

Diabetes mellitus (DM) is a global health issue of significance that must be diagnosed as early as possible and managed well. This study presents a framework for diabetes prediction using Machine Learning (ML) models, complemented with eXplainable Artificial Intelligence (XAI) tools, to investigate both the predictive accuracy and interpretability...

Successful software development hinges on effective communication and collaboration, which are significantly influenced by human and social dynamics. Poor management of these elements can lead to the emergence of ‘community smells’, i.e., negative patterns in socio-technical interactions that gradually accumulate as ‘social debt’. This issue is par...

Mining Software Repositories (MSR) has become a popular research area recently. MSR analyzes different sources of data, such as version control systems, code repositories, defect tracking systems, archived communication, deployment logs, and so on, to uncover interesting and actionable insights from the data for improved software development, maint...

COmmon Software Measurement International Consortium (COSMIC) Functional Size Measurement is a method widely used in the software industry to quantify user functionality and measure software size, which is crucial for estimating development effort, cost, and resource allocation. COSMIC measurement is a manual task that requires qualified profession...

Mining software repositories (MSR) has become a popular research area recently. MSR analyzes different sources of data, such as version control systems, code repositories, defect tracking systems, archived communication, deployment logs, and so on, to uncover interesting and actionable insights from the data for improved software development, maint...

Context: Software development is a complex socio-technical process requiring a deep understanding of various aspects. In order to support practitioners in understanding such a complex activity, repository process metrics, like number of pull requests and issues, emerged as crucial for evaluating CI/CD workflows and guiding informed decision-making....

As machine learning (ML) systems are increasingly adopted across industries, addressing fairness and bias has become essential. While many solutions focus on ethical challenges in ML, recent studies highlight that data itself is a major source of bias. Pre-processing techniques, which mitigate bias before training, are effective but may impact mode...

This systematic review explores the use of machine learning (ML) in predicting diabetes, focusing on datasets, algorithms, training methods, and evaluation metrics. It examines datasets like the Singapore National Diabetic Retinopathy Screening program, REPLACE-BG, National Health and Nutrition Examination Survey, and Pima Indians Diabetes Database...

As the adoption of machine learning (ML) systems continues to grow across industries, concerns about fairness and bias in these systems have taken center stage. Fairness toolkits, designed to mitigate bias in ML models, serve as critical tools for addressing these ethical concerns. However, their adoption in the context of software development rema...

Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous research has demonstrated their harmfulness for test code maintainability and effectiveness, showing their impact on test code quality. As such, the quality of test cases affected by test smells is likely to deviate significantly from the quality of...

Stakeholders' conversations in requirements elicitation meetings contain valuable information, but manually extracting system requirements from these discussions is a time-consuming and labor-intensive task, and there is a risk of errors and the introduction of biases. While current methods assist in summarizing conversations and classifying requir...

Bots are software systems designed to support users by automating specific processes, tasks, or activities. When these systems implement a conversational component to interact with users, they are also known as conversational agents or chatbots . Bots—particularly in their conversation-oriented version and AI-powered—have seen increased adoption ov...

Refactoring has been widely investigated in the past in relation to production code quality, yet little is known about how developers apply refactoring to test code. Specifically, there is still a lack of investigation into how developers typically refactor test code and its effects on test code quality and effectiveness. This paper presents an exp...

Context: The emergence of Large Language Models (LLMs) has significantly transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories. Objectives: Our objective is to establish a practical framework for future SE researchers needing to enhance the data collection and dataset while conducting software rep...

This paper explores the integration of Generative Artificial Intelligence (GAI) in education, focusing on the pivotal role of teachers. It introduces current policies from educational organizations and governmental institutions, and briefly reports on research evidence on the topic, emphasizing the need for targeted training programs. These program...

Nowadays, virtual worlds are evolving into immersive metaverses, i.e., collective, shared virtual spaces that arise from the convergence of virtual reality (VR), augmented reality (AR), the internet, and additional digital technologies. These environments allow users to interact through avatars. While research has explored the technologies and soci...

As machine learning (ML) and artificial intelligence (AI) technologies become increasingly prevalent in society, concerns about their environmental sustainability have grown. Developing and deploying ML-enabled systems, especially during training and inference, are resource-intensive, raising sustainability issues. Green AI has emerged as a respons...

Bots are software systems designed to support users by automating a specific process, task, or activity. When such systems implement a conversational component to interact with the users, they are also known as conversational agents. Bots, particularly in their conversation-oriented version and AI-powered, have seen their adoption increase over tim...

The metaverse has transitioned from a science fiction term to a rapidly growing area of research and application, with potential uses in education, professional training, social events, and the virtual economy. However, despite this progress, a fully realized and functional metaverse is not yet available, and its development still requires a clear...

Background. Shortening development cycles and pressure mounting to continuously deliver releases make developers resort to expedients, shortcuts, and, ultimately, detrimental design choices, resulting in intricate and difficult-to-maintain software systems. A classical solution is code refactoring to reduce code complexity and aid maintainability....

As a socio-technical activity, software development involves the close interconnection of people and technology. The integration of Large Language Models (LLMs) into this process exemplifies the socio-technical nature of software development. Although LLMs influence the development process, software development remains fundamentally human-centric,...

Machine learning's widespread adoption in decision-making processes raises concerns about fairness, particularly regarding the treatment of sensitive features and potential discrimination against minorities. The software engineering community has responded by developing fairness-oriented metrics, empirical studies, and approaches. However, there re...

This paper introduces AGORA, an innovative approach that leverages Large Language Models to automate the definition of acceptance test cases from use cases. AGORA consists of two phases that exploit prompt engineering to 1) identify test cases for specific use cases and 2) generate detailed acceptance tests cases. AGORA was evaluated through a cont...

To provide a measurable representation of social issues in software teams, the research community defined a set of anti-patterns that may lead to the emergence of both social and technical debt, i.e., “community smells”. Researchers have investigated community smells from different perspectives; in particular, they have analyzed how product-related...

Quantum computing is an emerging field in which theoretical principles are being transformed into practical applications, largely due to the efforts of the developer community. In order to ensure that quantum software engineering continues to advance, it is vital to understand the experiences, challenges, and aspirations of developers. This chapter...

Context. Source code refactoring is a well-established approach to improving source code quality without compromising its external behavior. Motivation. The literature described the benefits of refactoring, yet its application in practice is threatened by the high cost of time, resource allocation, and effort required to perform it continuously. Pr...

Abstract Context: Artificial Intelligence (AI) is pervasive in several application domains and promises to be even more diffused in the next decades. Developing high-quality AI-enabled systems — software systems embedding one or multiple AI components, algorithms, and models — could introduce critical challenges for mitigating specific risks relate...

Cloud computing has become integral to modern organizational operations, offering efficiency and agility. However, security challenges such as data loss and downtime necessitate tailored compliance solutions. Risk assessment is crucial for identifying and mitigating cloud-related threats, yet a standardized approach remains elusive. Our study aims...

Context: The term metaverse refers to a persistent, virtual, three-dimensional environment where individuals may communicate, engage, and collaborate. One of the most multifaceted and challenging use cases of the metaverse is education, where educators and learners may require multiple technical, social, psychological, and interaction instruments t...

Unit testing verifies the presence of faults in individual software components. Previous research has been targeting the automatic generation of unit tests through the adoption of random or search-based algorithms. Despite their effectiveness, these approaches aim at creating tests by solely optimizing metrics like code coverage, without ensuring t...

Quantum computing is a promising field that can solve complex problems beyond traditional computers’ capabilities. Developing high-quality quantum software applications, called quantum software engineering, has recently gained attention. However, quantum software development faces challenges related to code quality. A recent study found that many o...

Artificial Intelligence (AI) is rapidly advancing with a data-centered approach suitable for various domains. Nevertheless, AI faces significant challenges, particularly in data quality. Data collection from diverse sources can introduce quality issues that may threaten the development of AI-enabled systems. A growing concern in this context is the...

With the rate of discovered and disclosed vulnerabilities escalating, researchers have been experimenting with machine learning to predict whether a vulnerability will be exploited. Existing solutions leverage information unavailable when a CVE is created, making them unsuitable just after the disclosure. This paper experiments with early exploitab...

Test smells are symptoms of sub-optimal design choices adopted when developing test cases. Previous studies have proved their harmfulness for test code maintainability and effectiveness. Therefore, researchers have been proposing automated, heuristic-based techniques to detect them. However, the performance of these detectors is still limited and d...

Software quality is critical, as low quality, or "Code smell," increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and manages code smells by learning from diverse and distributed data sources while respecting privacy and providing a scalable solution for continuously integrating new pattern...

Test flakiness arises when a test case exhibits inconsistent behavior by alternating between passing and failing states when executed against the same code. Previous research showed the significance of the problem in practice, proposing empirical studies into the nature of flakiness and automated techniques for its detection. Machine learning model...

Software reusability mechanisms, like inheritance and delegation in Object-Oriented programming, are widely recognized as key instruments of software design that reduce the risks of source code being affected by defects, other than to reduce the effort required to maintain and evolve source code. Previous work has traditionally employed source code...

Machine learning is part of the daily life of people and companies worldwide. Unfortunately, bias in machine learning algorithms risks unfairly influencing the decision-making process and reiterating possible discrimination. While the interest of the software engineering community in software fairness is rapidly increasing, there is still a lack of...

Published to Journal of Systems and Software Link: https://doi.org/10.1016/j.jss.2024.112151 Context: Artificial Intelligence (AI) is pervasive in several application domains and promises to be even more diffused in the next decades. The development of high-quality AI-enabled systems, i.e., software systems embedding one or multiple AI components,...

Estimating and understanding software development productivity represent crucial tasks for researchers and practitioners. Although different works focused on evaluating the impact of human factors on productivity, a few explored the influence of cultural/geographical diversity in software development communities. More particularly, all previous tre...

The software industry has rapidly increased in complexity and scale, leading to challenges in managing information and tasks among developer teams, often resulting in inefficiencies, misunderstandings, and delays. Moreover, the increasing search for automated tasks led to the extensive adoption of chatbots—a.k.a. conversational agents—for software...

UML use cases are commonly used in software engineering to specify the functional requirements of a system since they are an effective tool for interacting with stakeholders thanks to the use of natural languages. However, producing high-quality use cases can be challenging due to the lack of precise guidelines and suitable tools. This can lead to...

Context. Refactoring has been widely investigated in the past in relation to production code quality, yet still little is known on how developers apply refactoring on test code. Specifically, there is still a lack of investigation into how developers typically refactor test code and its effects on test code quality and effectiveness. Objective. Thi...

The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a dataset for detecting smells highly depends on relevant properties such as the size, severity level, project types, number of each type of smell, number of smells, and the...

Context. Quantum computing is becoming a reality, and quantum software engineering (QSE) is emerging as a new discipline to enable developers to design and develop quantum programs. Objective. This paper presents a systematic mapping study of the current state of QSE research, aiming to identify the most investigated topics, the types and number of...

This paper proposes a Federated Learning Code Smell Detection (FedCSD) approach that allows organizations to collaboratively train federated ML models while preserving their data privacy. These assertions have been supported by three experiments that have significantly leveraged three manually validated datasets aimed at detecting and examining dif...

Software refactoring is a behavior-preserving activity to improve the source code quality without changing its external behavior. Unfortunately, it is often a manual and error-prone task that may induce regressions in the source code. Researchers have provided initial compelling evidence of the relation between refactoring and defects, yet little i...

In the era of social distancing, distance learning represents a crucial educational challenge. Several 2D information technologies have been provided, yet these share multiple limitations and have negative social, educational, and psychological implications for learners. Metaverse promises to revolutionize education as we know it: this is a persist...

Modern software systems are increasingly complex and assembled with the use of different technologies, third-party libraries, and more. As a result, the risk of falling into security concerns is high whether such systems are not developed with a proper security mindset. Despite the security verification approaches proposed by researchers and tool...

The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a dataset for detecting smells highly depends on relevant properties such as the size, severity level, project types, number of each type of smell, number of smells, and the...

Nowadays, machine learning is being used to address multiple problems in various research fields, with software engineering researchers being among the most active users of machine learning mechanisms. Recent advances revolve around the use of quantum machine learning, which promises to revolutionize program computation and boost software systems’...

The First Working Seminar on Quantum Software Engineering (WSQSE 2022) took place on December 15 and 16, 2022 in Innsbruck, Austria. An audience of 33 quantum computing and software engineering researchers and practitioners joined the two-day event. The workshop enabled us to foresee short-term and longterms perspectives of Quantum Software Enginee...

MLOps tools enable continuous development of machine learning, following the DevOps process. Different MLOps tools have been presented on the market, however, such a number of tools often create confusion on the most appropriate tool to be used in each DevOps phase. To overcome this issue, we conducted a multivocal literature review mapping 84 MLOp...

Infrastructure‐as‐code (IaC) helps keep up with the demand for fast, reliable, high‐quality services by provisioning and managing infrastructures through configuration files. Those files ensure efficient and repeatable routines for system provisioning, but they might be affected by code smells that negatively affect quality and code maintenance. Re...

The concept of metaverse is becoming pervasive and promises to revolutionize the way people will interact with each other in a sustainable manner. The educational context seems to represent an ideal use case, as the metaverse may provide a digital environment empowered by analytical instruments able to monitor the social and psychological needs of...

Background Developers use Static Analysis Tools (SATs) to control for potential quality issues in source code, including defects and technical debt. Tool vendors have devised quite a number of tools, which makes it harder for practitioners to select the most suitable one for their needs. To better support developers, researchers have been conductin...

Dark Patterns are design patterns used to trick users into acting against their real interest. The web provides an infinite number of services accessible to anyone, which do not always promote a good user experience and are often structured with the aim of leading the user to perform unwanted actions or discourage him from making decisions that cou...

In recent years, job advertisements through the web or social media represent an easy way to spread this information. However, social media are often a dangerous showcase of possibly labor exploitation advertisements. This paper aims to determine the potential indicators of labor exploitation for unskilled jobs offered in the Netherlands. Specifica...

Test flakiness is a phenomenon occurring when a test case is non-deterministic and exhibits both a passing and failing behavior when run against the same code. Over the last years, the problem has been closely investigated by researchers and practitioners, who all have shown its relevance in practice. The software engineering research community has...