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Found 7 papers, 4 papers with code
When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference
These findings motivate our exploration of dynamic inference in code completion and inspire us to enhance it with a decision-making mechanism that stops the generation of incorrect code.
An Extensive Study on Adversarial Attack against Pre-trained Models of Code
Although several approaches have been proposed to generate adversarial examples for PTMC, the effectiveness and efficiency of such approaches, especially on different code intelligence tasks, has not been well understood.
FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software
The effectiveness and efficiency of these systems heavily depend on the quality of the training datasets.
Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems
The experimental results show that the proposed estimator helps save 23. 3% of computational cost measured in floating-point operations for the code completion systems, and 80. 2% of rejected prompts lead to unhelpful completion
Is this Change the Answer to that Problem? Correlating Descriptions of Bug and Code Changes for Evaluating Patch Correctness
To tackle this problem, our intuition is that natural language processing can provide the necessary representations and models for assessing the semantic correlation between a bug (question) and a patch (answer).
A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits
Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits.
Software Engineering
Attention Please: Consider Mockito when Evaluating Newly Proposed Automated Program Repair Techniques
Our findings show that: 1) Mockito bugs are not more complex for repairing compared with bugs from other projects; 2) the bugs repaired by the state-of-the-art tools share the same repair patterns compared with those patterns required to repair Mockito bugs; however, 3) the state-of-the-art tools perform poorly on Mockito bugs (Nopol can only correctly fix one bug while SimFix and CapGen cannot fix any bug in Mockito even if all the buggy locations have been exposed).
Software Engineering
