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Found 10 papers, 5 papers with code
Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?
Upon evolving their software, organizations and individual developers have to spend a substantial effort to pay back technical debt, i. e., the fact that software is released in a shape not as good as it should be, e. g., in terms of functionality, reliability, or maintainability.
Automating Code-Related Tasks Through Transformers: The Impact of Pre-training
Then, we pre-train 32 transformers using both (i) generic pre-training objectives usually adopted in SE; and (ii) pre-training objectives tailored to specific code-related tasks subject of our experimentation, namely bug-fixing, code summarization, and code completion.
Evaluating SZZ Implementations Through a Developer-informed Oracle
Once built, we used the oracle to evaluate several variants of the SZZ algorithm in terms of their accuracy.
Software Engineering
Why Developers Refactor Source Code: A Mining-based Study
Our results led to (i) quantitative evidence of the relationship existing between certain process/product metrics and refactoring operations and (ii) a detailed taxonomy, generalizing and complementing the ones existing in the literature, of motivations pushing developers to refactor source code.
Software Engineering
Automated Identification of On-hold Self-admitted Technical Debt
Self-admitted technical debt (SATD) is a particular form of technical debt: developers consciously perform the hack but also document it in the code by adding comments as a reminder (or as an admission of guilt).
Software Engineering
DeepMutation: A Neural Mutation Tool
To this aim, two characteristics of mutation testing frameworks are of paramount importance: (i) they should generate mutants that are representative of real faults; and (ii) they should provide a complete tool chain able to automatically generate, inject, and test the mutants.
Taxonomy of Real Faults in Deep Learning Systems
The growing application of deep neural networks in safety-critical domains makes the analysis of faults that occur in such systems of enormous importance.
On Learning Meaningful Code Changes via Neural Machine Translation
We show that, when applied in a narrow enough context (i. e., small/medium-sized pairs of methods before/after the pull request changes), NMT can automatically replicate the changes implemented by developers during pull requests in up to 36% of the cases.
Learning How to Mutate Source Code from Bug-Fixes
Starting from code fixed by developers in the context of a bug-fix, our empirical evaluation showed that our models are able to predict mutants that resemble original fixed bugs in between 9% and 45% of the cases (depending on the model).
Software Engineering
An Empirical Study on Android-related Vulnerabilities
Most of these studies focused on vulnerabilities that could affect mobile apps, while just few investigated vulnerabilities affecting the underlying platform on which mobile apps run: the Operating System (OS).
Software Engineering Cryptography and Security
