Mauro Pezzè
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View article: E-Test: E'er-Improving Test Suites
E-Test: E'er-Improving Test Suites Open
Test suites are inherently imperfect, and testers can always enrich a suite with new test cases that improve its quality and, consequently, the reliability of the target software system. However, finding test cases that explore execution s…
View article: Get on the Train or be Left on the Station: Using LLMs for Software Engineering Research
Get on the Train or be Left on the Station: Using LLMs for Software Engineering Research Open
The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range fr…
View article: A 2030 Roadmap for Software Engineering
A 2030 Roadmap for Software Engineering Open
The landscape of software engineering has dramatically changed in recent years. The impressive advances of artificial intelligence are just the latest and most disruptive innovation that has remarkably changed the software engineering rese…
View article: Tratto: A Neuro-Symbolic Approach to Deriving Axiomatic Test Oracles
Tratto: A Neuro-Symbolic Approach to Deriving Axiomatic Test Oracles Open
This paper presents Tratto, a neuro-symbolic approach that generates assertions (boolean expressions) that can serve as axiomatic oracles, from source code and documentation. The symbolic module of Tratto takes advantage of the grammar of …
View article: Software Engineering by and for Humans in an AI Era
Software Engineering by and for Humans in an AI Era Open
The landscape of software engineering is undergoing a transformative shift driven by advancements in machine learning, Artificial Intelligence (AI), and autonomous systems. This roadmap article explores how these technologies are reshaping…
View article: Editorial: The End of the Journey
Editorial: The End of the Journey Open
View article: Predicting Failures of Autoscaling Distributed Applications
Predicting Failures of Autoscaling Distributed Applications Open
Predicting failures in production environments allows service providers to activate countermeasures that prevent harming the users of the applications. The most successful approaches predict failures from error states that the current appr…
View article: Generative AI in Software Engineering Must Be Human-Centered: The Copenhagen Manifesto
Generative AI in Software Engineering Must Be Human-Centered: The Copenhagen Manifesto Open
View article: Semantic matching in GUI test reuse
Semantic matching in GUI test reuse Open
Reusing test cases across apps that share similar functionalities reduces both the effort required to produce useful test cases and the time to offer reliable apps to the market. The main approaches to reuse test cases across apps combine …
View article: Editorial: ICSE and the Incredible Contradictions of Software Engineering
Editorial: ICSE and the Incredible Contradictions of Software Engineering Open
This report gives an overview of the 6th ICSE Workshop on Component-Based Software Engineering held at 25th International Conference on Software Engineering. The workshop brought together researchers and practitioners from three communitie…
View article: Editorial: Toward the Future with Eight Issues Per Year
Editorial: Toward the Future with Eight Issues Per Year Open
Toward the Future with Eight Issues Per YearI am pleased to make several announcements with this issue of ACM Transactions on Software Engineering and Methodology (TOSEM), including the publication of eight issues per year, the implementat…
View article: Prevent: An Unsupervised Approach to Predict Software Failures in Production
Prevent: An Unsupervised Approach to Predict Software Failures in Production Open
This paper presents Prevent, a fully unsupervised approach to predict and localize failures in distributed enterprise applications. Software failures in production are unavoidable. Predicting failures and locating failing components online…
View article: Prevent: An Unsupervised Approach to Predict Software Failures in Production
Prevent: An Unsupervised Approach to Predict Software Failures in Production Open
Prevent combines the elements of machine learning and statistical data analysis into a unique approach to predict failures and identify their root causes in complex, dynamic software systems, all without the need for predetermined rules or…
View article: Prevent: An Unsupervised Approach to Predict Software Failures in Production
Prevent: An Unsupervised Approach to Predict Software Failures in Production Open
Prevent combines the elements of machine learning and statistical data analysis into a unique approach to predict failures and identify their root causes in complex, dynamic software systems, all without the need for predetermined rules or…
View article: Prevent: An Unsupervised Approach to Predict Software Failures in Production
Prevent: An Unsupervised Approach to Predict Software Failures in Production Open
Prevent combines the elements of machine learning and statistical data analysis into a unique approach to predict failures and identify their root causes in complex, dynamic software systems, all without the need for predetermined rules or…
View article: Prevent: An Unsupervised Approach to Predict Software Failures in Production
Prevent: An Unsupervised Approach to Predict Software Failures in Production Open
Prevent combines the elements of machine learning and statistical data analysis into a unique approach to predict failures and identify their root causes in complex, dynamic software systems, all without the need for predetermined rules or…
View article: Prevent: An Unsupervised Approach to Predict Software Failures in Production
Prevent: An Unsupervised Approach to Predict Software Failures in Production Open
Prevent combines the elements of machine learning and statistical data analysis into a unique approach to predict failures and identify their root causes in complex, dynamic software systems, all without the need for predetermined rules or…
View article: Prevent: An Unsupervised Approach to Predict Software Failures in Production
Prevent: An Unsupervised Approach to Predict Software Failures in Production Open
Prevent combines the elements of machine learning and statistical data analysis into a unique approach to predict failures and identify their root causes in complex, dynamic software systems, all without the need for predetermined rules or…
View article: EDITORIAL: Announcing Six TOSEM Issues Per Year
EDITORIAL: Announcing Six TOSEM Issues Per Year Open
As the eagle-eyed among you may have noticed, ACM TOSEM has a new editorial charter. Approved by both the TOSEM editorial board and the ACM Publications Board, it is now posted to the TOSEM website.
View article: Semantic Matching in GUI Test Reuse
Semantic Matching in GUI Test Reuse Open
This repository provides the replication package of our paper "Semantic Matching in GUI Test Reuse" submitted to the Empirical Software Engineering 2023. It includes the codes, results and input data.
View article: Semantic Matching in GUI Test Reuse
Semantic Matching in GUI Test Reuse Open
This repository provides the replication package of our paper "Semantic Matching in GUI Test Reuse" submitted to the Empirical Software Engineering 2023. It includes the codes, results and input data.
View article: Call Me Maybe: Using NLP to Automatically Generate Unit Test Cases Respecting Temporal Constraints
Call Me Maybe: Using NLP to Automatically Generate Unit Test Cases Respecting Temporal Constraints Open
A class may need to obey temporal constraints in order to function correctly. For example, the correct usage protocol for an iterator is to always check whether there is a next element before asking for it; iterating over a collection when…
View article: PREVENT: An Unsupervised Approach to Predict Software Failures in Production
PREVENT: An Unsupervised Approach to Predict Software Failures in Production Open
This paper presents PREVENT, an approach for predicting and localizing failures in distributed enterprise applications by combining unsupervised techniques. Software failures can have dramatic consequences in production, and thus predictin…
View article: The ineffectiveness of domain-specific word embedding models for GUI test reuse
The ineffectiveness of domain-specific word embedding models for GUI test reuse Open
Reusing test cases across similar applications can significantly reduce testing effort. Some recent test reuse approaches successfully exploit word embedding models to semantically match GUI events across Android apps. It is a common under…
View article: PREVENT: A Semi-Supervised Approach to Predict Software Failures in Production new
PREVENT: A Semi-Supervised Approach to Predict Software Failures in Production new Open
This repository provides the replication package of the "PREVENT: A Semi-Supervised Approach to Predict Software Failures in Production" paper. This repository provides the replication package of the "PREVENT: A Semi-Supervised Approach to…
View article: The Ineffectiveness of Domain Specific Word Embedding Models for GUI Test Reuse
The Ineffectiveness of Domain Specific Word Embedding Models for GUI Test Reuse Open
This repository provides the replication package of our paper "The Ineffectiveness of Domain Specific Word Embedding Models for GUI Test Reuse" submitted to the 30th IEEE/ACM International Conference on Program Comprehension (ICPC 2022). I…
View article: The Ineffectiveness of Domain Specific Word Embedding Models for GUI Test Reuse
The Ineffectiveness of Domain Specific Word Embedding Models for GUI Test Reuse Open
This repository provides the replication package of our paper "The Ineffectiveness of Domain Specific Word Embedding Models for GUI Test Reuse" submitted to the 30th IEEE/ACM International Conference on Program Comprehension (ICPC 2022). I…
View article: PREVENT: A Semi-Supervised Approach to Predict Software Failures in Production
PREVENT: A Semi-Supervised Approach to Predict Software Failures in Production Open
This repository provides the replication package of our paper "PREVENT: A Semi-Supervised Approach to Predict Software Failures in Production". It includes the codes, results, inputs and train datasets.
View article: PREVENT: A Semi-Supervised Approach to Predict Software Failures in Production
PREVENT: A Semi-Supervised Approach to Predict Software Failures in Production Open
This repository provides the replication package of our paper "PREVENT: A Semi-Supervised Approach to Predict Software Failures in Production". It includes the codes, results, inputs and train datasets.
View article: MeMo: Automatically identifying metamorphic relations in Javadoc comments for test automation
MeMo: Automatically identifying metamorphic relations in Javadoc comments for test automation Open
Software testing depends on effective oracles. Implicit oracles, such as checks for program crashes, are widely applicable but narrow in scope. Oracles based on formal specifications can reveal application-specific failures, but specificat…