arXiv subjects

Dario Di Nucci

Publications and source records attributed to Dario Di Nucci.

24 records · Page 2Linked to original sources

arXiv2022

Software Engineering for Quantum Programming: How Far Are We?

Quantum computing is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially overcome the limits of classical computation. Over the last years, all major companies have provided frameworks and programming languages that allow developers to create their quantum applications. This shift has led to the definition of a new discipline called quantum software engineering, which is demanded to define novel methods for engineering large-scale quantum applications. While the research community is successfully embracing this call, we notice a lack of systematic investigations into the state of the practice of quantum programming. Understanding the challenges that quantum developers face is vital to precisely define the aims of quantum software engineering. Hence, in this paper, we first mine all the GitHub repositories that make use of the most used quantum programming frameworks currently on the market and then conduct coding analysis sessions to produce a taxonomy of the purposes for which quantum technologies are used. In the second place, we conduct a survey study that involves the contributors of the considered repositories, which aims to elicit the developers' opinions on the current adoption and challenges of quantum programming. On the one hand, the results highlight that the current adoption of quantum programming is still limited. On the other hand, there are many challenges that the software engineering community should carefully consider: these do not strictly pertain to technical concerns but also socio-technical matters.

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arXiv2021

RepoMiner: a Language-agnostic Python Framework to Mine Software Repositories for Defect Prediction

Data originating from open-source software projects provide valuable information to enhance software quality. In the scope of Software Defect Prediction, one of the most challenging parts is extracting valid data about failure-prone software components from these repositories, which can help develop more robust software. In particular, collecting data, calculating metrics, and synthesizing results from these repositories is a tedious and error-prone task, which often requires understanding the programming languages involved in the mined repositories, eventually leading to a proliferation of language-specific data-mining software. This paper presents RepoMiner, a language-agnostic tool developed to support software engineering researchers in creating datasets to support any study on defect prediction. RepoMiner automatically collects failure data from software components, labels them as failure-prone or neutral, and calculates metrics to be used as ground truth for defect prediction models. We present its implementation and provide examples of its application.

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arXiv2021

Detecting Security Fixes in Open-Source Repositories using Static Code Analyzers

The sources of reliable, code-level information about vulnerabilities that affect open-source software (OSS) are scarce, which hinders a broad adoption of advanced tools that provide code-level detection and assessment of vulnerable OSS dependencies. In this paper, we study the extent to which the output of off-the-shelf static code analyzers can be used as a source of features to represent commits in Machine Learning (ML) applications. In particular, we investigate how such features can be used to construct embeddings and train ML models to automatically identify source code commits that contain vulnerability fixes. We analyze such embeddings for security-relevant and non-security-relevant commits, and we show that, although in isolation they are not different in a statistically significant manner, it is possible to use them to construct a ML pipeline that achieves results comparable with the state of the art. We also found that the combination of our method with commit2vec represents a tangible improvement over the state of the art in the automatic identification of commits that fix vulnerabilities: the ML models we construct and commit2vec are complementary, the former being more generally applicable, albeit not as accurate.

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arXiv2021

Prioritising Server Side Reachability via Inter-process Concolic Testing

Context: Most approaches to automated white-box testing consider the client side and the server side of a web application in isolation from each other. Such testers lack a whole-program perspective on the web application under test. Inquiry: We hypothesise that an additional whole-program perspective would enable the tester to discover which server side errors can be triggered by an actual end user accessing the application through the client, and which ones can only be triggered in hypothetical scenarios. Approach: In this paper, we explore the idea of employing such a whole-program perspective in testing. To this end, we develop , a novel concolic tester which operates on full-stack JavaScript web applications, where both the client and the server side are JavaScript processes communicating via asynchronous messages -- as enabled by the WebSocket or Socket.IO-libraries. Knowledge: We find that the whole-program perspective enables discerning high-priority errors, which are reachable from a particular client, from low-priority errors, which are not accessible through the tested client. Another benefit of the perspective is that it allows the automated tester to construct practical, step-by-step scenarios for triggering server side errors from the end user's perspective. Grounding: We apply on a collection of web applications to evaluate how effective testing is in distinguishing between high- and low-priority errors. The results show that correctly classifies the majority of server errors. Importance: This paper demonstrates the feasibility of testing as a novel approach for automatically testing web applications. Classifying errors as being of high or low importance aids developers in prioritising bugs that might be encountered by users, and postponing the diagnosis of bugs that are less easily reached.

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arXiv2020

DeepIaC: Deep Learning-Based Linguistic Anti-pattern Detection in IaC

Linguistic anti-patterns are recurring poor practices concerning inconsistencies among the naming, documentation, and implementation of an entity. They impede readability, understandability, and maintainability of source code. This paper attempts to detect linguistic anti-patterns in infrastructure as code (IaC) scripts used to provision and manage computing environments. In particular, we consider inconsistencies between the logic/body of IaC code units and their names. To this end, we propose a novel automated approach that employs word embeddings and deep learning techniques. We build and use the abstract syntax tree of IaC code units to create their code embedments. Our experiments with a dataset systematically extracted from open source repositories show that our approach yields an accuracy between0.785and0.915in detecting inconsistencies

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arXiv2020

Towards a Catalogue of Software Quality Metrics for Infrastructure Code

Infrastructure-as-code (IaC) is a practice to implement continuous deployment by allowing management and provisioning of infrastructure through the definition of machine-readable files and automation around them, rather than physical hardware configuration or interactive configuration tools. On the one hand, although IaC represents an ever-increasing widely adopted practice nowadays, still little is known concerning how to best maintain, speedily evolve, and continuously improve the code behind the IaC practice in a measurable fashion. On the other hand, source code measurements are often computed and analyzed to evaluate the different quality aspects of the software developed. However, unlike general-purpose programming languages (GPLs), IaC scripts use domain-specific languages, and metrics used for GPLs may not be applicable for IaC scripts. This article proposes a catalogue consisting of 46 metrics to identify IaC properties focusing on Ansible, one of the most popular IaC language to date, and shows how they can be used to analyze IaC scripts.

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