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Automated variable renaming: are we there yet?

EMPIRICAL SOFTWARE ENGINEERING 2023年第2期28卷 1-26页

作者： Mastropaolo, Antonio Aghajani, Emad Pascarella, Luca Bavota, Gabriele Univ Svizzera italiana SEART Software Inst Lugano Switzerland

Identifiers, such as method and variable names, form a large portion of source code. Therefore, low-quality identifiers can substantially hinder code comprehension. To support developers in using meaningful identifiers, several (semi-)automatic techniques have been proposed, mostly being data-driven (e.g., statistical language models, deep learning models) or relying on static code analysis. Still, limited empirical investigations have been performed on the effectiveness of such techniques for recommending developers with meaningful identifiers, possibly resulting in rename refactoring operations. We present a large-scale study investigating the potential of data-driven approaches to support automated variable renaming. We experiment with three state-of-the-art techniques: a statistical language model and two DL-based models. The three approaches have been trained and tested on three datasets we built with the goal of evaluating their ability to recommend meaningful variable identifiers. Our quantitative and qualitative analyses show the potential of such techniques that, under specific conditions, can provide valuable recommendations and are ready to be integrated in rename refactoring tools. Nonetheless, our results also highlight limitations of the experimented approaches that call for further research in this field.

关键词： code comprehension Empirical Study machine learning on code

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machine-learning Supported Vulnerability Detection in Source code 2019

Machine-Learning Supported Vulnerability Detection in Source...

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27th ACM Joint Meeting on European Software Engineering Conference (ESEC) / Symposium on the Foundations of Software Engineering (FSE)

作者： Sonnekalb, Tim German Aerosp Ctr DLR Inst Data Sci Jena Germany

ISBN: (纸本)9781450355728

The awareness of writing secure code rises with the increasing number of attacks and their resultant damage. But often, software developers are no security experts and vulnerabilities arise unconsciously during the development process. They use static analysis tools for bug detection, which often come with a high false positive rate. The developers, therefore, need a lot of resources to mind about all alarms, if they want to consistently take care of the security of their software project. We want to investigate, if machine learning techniques could point the user to the position of a security weak point in the source code with a higher accuracy than ordinary methods with static analysis. For this purpose, we focus on current machine learning on code approaches for our initial studies to evolve an efficient way for finding security-related software bugs. We will create a configuration interface to discover certain vulnerabilities, categorized in CWEs. We want to create a benchmark tool to compare existing source code representations and machine learning architectures for vulnerability detection and develop a customizable feature model. At the end of this PhD project, we want to have an easy-to-use vulnerability detection tool based on machine learning on code.

关键词： software security vulnerabilities vulnerability detection source code analysis machine learning on code

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How the Training Procedure Impacts the Performance of Deep learning-based Vulnerability Patching 24

How the Training Procedure Impacts the Performance of Deep L...

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28th International Conference on Evaluation and Assessment in Software Engineering (EASE)

作者： Mastropaolo, Antonio Nardone, Vittoria Bavota, Gabriele Di Penta, Massimiliano Univ Svizzera Ialiana USI Lugano Switzerland Univ Molise Campobasso Italy Univ Sannio Benevento Italy

ISBN: (纸本)9798400717017

Generative deep learning (DL) models have been successfully adopted for vulnerability patching. However, such models require the availability of a large dataset of patches to learn from. To overcome this issue, researchers have proposed to start from models pre-trained with general knowledge, either on the programming language or on similar tasks such as bug fixing. Despite the efforts in the area of automated vulnerability patching, there is a lack of systematic studies on how these different training procedures impact the performance of DL models for such a task. This paper provides a manyfold contribution to bridge this gap, by (i) comparing existing solutions of self-supervised and supervised pre-training for vulnerability patching;and (ii) for the first time, experimenting with different kinds of prompt-tuning for this task. The study required to train/test 23 DL models. We found that a supervised pre-training focused on bug-fixing, while expensive in terms of data collection, substantially improves DL-based vulnerability patching. When applying prompt-tuning on top of this supervised pre-trained model, there is no significant gain in performance. Instead, prompt-tuning is an effective and cheap solution to substantially boost the performance of self-supervised pre-trained models, i.e., those not relying on the bug-fixing pre-training.

关键词： Software Vulnerability Repair Pre-Trained Models machine learning on code Prompt Tuning

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Using Pre-Trained Models to Boost code Review Automation 22

Using Pre-Trained Models to Boost Code Review Automation

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ACM/IEEE 44th International Conference on Software Engineering (ICSE)

作者： Tufano, Rosalia Masiero, Simone Mastropaolo, Antonio Pascarella, Luca Poshyvanyk, Denys Bavota, Gabriele Univ Svizzera Italiana SEART Software Inst Lugano Switzerland William & Mary SEMERU Comp Sci Dept Williamsburg VA USA

ISBN: (纸本)9781450392211

code review is a practice widely adopted in open source and industrial projects. Given the non-negligible cost of such a process, researchers started investigating the possibility of automating specific code review tasks. We recently proposed Deep learning (DL) models targeting the automation of two tasks: the first model takes as input a code submitted for review and implements in it changes likely to be recommended by a reviewer;the second takes as input the submitted code and a reviewer comment posted in natural language and automatically implements the change required by the reviewer. While the preliminary results we achieved are encouraging, both models had been tested in rather simple code review scenarios, substantially simplifying the targeted problem. This was also due to the choices we made when designing both the technique and the experiments. In this paper, we build on top of that work by demonstrating that a pre-trained Text-To-Text Transfer Transformer (T5) model can outperform previous DL models for automating code review tasks. Also, we conducted our experiments on a larger and more realistic (and challenging) dataset of code review activities.

关键词： code Review Empirical Study machine learning on code

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Using Deep learning to Generate Complete Log Statements 22

Using Deep Learning to Generate Complete Log Statements

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ACM/IEEE 44th International Conference on Software Engineering (ICSE)

作者： Mastropaolo, Antonio Pascarella, Luca Bavota, Gabriele Univ Svizzera Italiana Switzerland SEART Software Inst Lugano Switzerland

ISBN: (纸本)9781450392211

Logging is a practice widely adopted in several phases of the software lifecycle. For example, during software development log statements allow engineers to verify and debug the system by exposing fine-grained information of the running software. While the benefits of logging are undisputed, taking proper decisions about where to inject log statements, what information to log, and at which log level (e.g., error, warning) is crucial for the logging effectiveness. In this paper, we present LANCE (Log stAtemeNt reCommEnder), the first approach supporting developers in all these decisions. LANCE features a Text-To-Text-Transfer-Transformer (T5) model that has been trained on 6,894,456 Java methods. LANCE takes as input a Java method and injects in it a full log statement, including a human-comprehensible logging message and properly choosing the needed log level and the statement location. Our results show that LANCE is able to (i) properly identify the location in the code where to inject the statement in 65.9% of Java methods requiring it;(ii) selecting the proper log level in 66.2% of cases;and (iii) generate a completely correct log statement including a meaningful logging message in 15.2% of cases.

关键词： Logging Empirical Study machine learning on code

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