DapStep: Deep Assignee Prediction for Stack Trace Error rePresentation

• URL: http://arxiv.org/abs/2201.05256v1
• Date: Fri, 14 Jan 2022 00:16:57 GMT
• Title: DapStep: Deep Assignee Prediction for Stack Trace Error rePresentation
• Authors: Denis Sushentsev, Aleksandr Khvorov, Roman Vasiliev, Yaroslav Golubev, Timofey Bryksin
• Abstract summary: We propose new deep learning models to solve the bug triage problem. The models are based on a bidirectional recurrent neural network with attention and on a convolutional neural network. To improve the quality of ranking, we propose using additional information from version control system annotations.
• Score: 61.99379022383108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of finding the best developer to fix a bug is called bug triage. Most of the existing approaches consider the bug triage task as a classification problem, however, classification is not appropriate when the sets of classes change over time (as developers often do in a project). Furthermore, to the best of our knowledge, all the existing models use textual sources of information, i.e., bug descriptions, which are not always available. In this work, we explore the applicability of existing solutions for the bug triage problem when stack traces are used as the main data source of bug reports. Additionally, we reformulate this task as a ranking problem and propose new deep learning models to solve it. The models are based on a bidirectional recurrent neural network with attention and on a convolutional neural network, with the weights of the models optimized using a ranking loss function. To improve the quality of ranking, we propose using additional information from version control system annotations. Two approaches are proposed for extracting features from annotations: manual and using an additional neural network. To evaluate our models, we collected two datasets of real-world stack traces. Our experiments show that the proposed models outperform existing models adapted to handle stack traces. To facilitate further research in this area, we publish the source code of our models and one of the collected datasets.

Related papers

• SINDER: Repairing the Singular Defects of DINOv2 [61.98878352956125]
  Vision Transformer models trained on large-scale datasets often exhibit artifacts in the patch token they extract. We propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset.
  arXiv  Detail & Related papers  (2024-07-23T20:34:23Z)
• Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines [83.65380507372483]
  Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box. This paper shows how to leverage recent advances in NLP and multi-modal learning to augment a pre-trained model with search engine retrieval.
  arXiv  Detail & Related papers  (2023-11-29T05:33:28Z)
• Do Language Models Learn Semantics of Code? A Case Study in Vulnerability Detection [7.725755567907359]
  We analyze the models using three distinct methods: interpretability tools, attention analysis, and interaction matrix analysis. We develop two annotation methods which highlight the bug semantics inside the model's inputs. Our findings indicate that it is helpful to provide the model with information of the bug semantics, that the model can attend to it, and motivate future work in learning more complex path-based bug semantics.
  arXiv  Detail & Related papers  (2023-11-07T16:31:56Z)
• An Effective Data-Driven Approach for Localizing Deep Learning Faults [20.33411443073181]
  We propose a novel data-driven approach that leverages model features to learn problem patterns. Our methodology automatically links bug symptoms to their root causes, without the need for manually crafted mappings. Our results demonstrate that our technique can effectively detect and diagnose different bug types.
  arXiv  Detail & Related papers  (2023-07-18T03:28:39Z)
• Debugging using Orthogonal Gradient Descent [7.766921168069532]
  Given a trained model that is partially faulty, can we correct its behaviour without having to train the model from scratch? In other words, can we " neural networks similar to how we address bugs in our mathematical models and standard computer code?
  arXiv  Detail & Related papers  (2022-06-17T00:03:54Z)
• Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future [63.99570204416711]
  We reimplement 18 methods for detecting potential annotation errors and evaluate them on 9 English datasets. We define a uniform evaluation setup including a new formalization of the annotation error detection task. We release our datasets and implementations in an easy-to-use and open source software package.
  arXiv  Detail & Related papers  (2022-06-05T22:31:45Z)
• Optimizing Active Learning for Low Annotation Budgets [6.753808772846254]
  In deep learning, active learning is usually implemented as an iterative process in which successive deep models are updated via fine tuning. We tackle this issue by using an approach inspired by transfer learning. We introduce a novel acquisition function which exploits the iterative nature of AL process to select samples in a more robust fashion.
  arXiv  Detail & Related papers  (2022-01-18T18:53:10Z)
• X-model: Improving Data Efficiency in Deep Learning with A Minimax Model [78.55482897452417]
  We aim at improving data efficiency for both classification and regression setups in deep learning. To take the power of both worlds, we propose a novel X-model. X-model plays a minimax game between the feature extractor and task-specific heads.
  arXiv  Detail & Related papers  (2021-10-09T13:56:48Z)
• Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach [80.8446673089281]
  We propose a new learning paradigm with graph representation and learning. Our framework contains two modules: 1) a backbone network (e.g., feedforward neural nets) as a lower model takes features as input and outputs predicted labels; 2) a graph neural network as an upper model learns to extrapolate embeddings for new features via message passing over a feature-data graph built from observed data.
  arXiv  Detail & Related papers  (2021-10-09T09:02:45Z)
• An Efficient Method of Training Small Models for Regression Problems with Knowledge Distillation [1.433758865948252]
  We propose a new formalism of knowledge distillation for regression problems. First, we propose a new loss function, teacher outlier loss rejection, which rejects outliers in training samples using teacher model predictions. By considering the multi-task network, training of the feature extraction of student models becomes more effective.
  arXiv  Detail & Related papers  (2020-02-28T08:46:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.