Identifying Technical Debt and Its Types Across Diverse Software Projects Issues

• URL: http://arxiv.org/abs/2408.09128v1
• Date: Sat, 17 Aug 2024 07:46:54 GMT
• Title: Identifying Technical Debt and Its Types Across Diverse Software Projects Issues
• Authors: Karthik Shivashankar, Mili Orucevic, Maren Maritsdatter Kruke, Antonio Martini,
• Abstract summary: Technical Debt (TD) identification in software projects issues is crucial for maintaining code quality, reducing long-term maintenance costs, and improving overall project health. This study advances TD classification using transformer-based models, addressing the critical need for accurate and efficient TD identification in large-scale software development.
• Score: 4.6173290119212265
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Technical Debt (TD) identification in software projects issues is crucial for maintaining code quality, reducing long-term maintenance costs, and improving overall project health. This study advances TD classification using transformer-based models, addressing the critical need for accurate and efficient TD identification in large-scale software development. Our methodology employs multiple binary classifiers for TD and its type, combined through ensemble learning, to enhance accuracy and robustness in detecting various forms of TD. We train and evaluate these models on a comprehensive dataset from GitHub Archive Issues (2015-2024), supplemented with industrial data validation. We demonstrate that in-project fine-tuned transformer models significantly outperform task-specific fine-tuned models in TD classification, highlighting the importance of project-specific context in accurate TD identification. Our research also reveals the superiority of specialized binary classifiers over multi-class models for TD and its type identification, enabling more targeted debt resolution strategies. A comparative analysis shows that the smaller DistilRoBERTa model is more effective than larger language models like GPTs for TD classification tasks, especially after fine-tuning, offering insights into efficient model selection for specific TD detection tasks. The study also assesses generalization capabilities using metrics such as MCC, AUC ROC, Recall, and F1 score, focusing on model effectiveness, fine-tuning impact, and relative performance. By validating our approach on out-of-distribution and real-world industrial datasets, we ensure practical applicability, addressing the diverse nature of software projects.

Related papers

• A Multitask Deep Learning Model for Classification and Regression of Hyperspectral Images: Application to the large-scale dataset [44.94304541427113]
  We propose a multitask deep learning model to perform multiple classification and regression tasks simultaneously on hyperspectral images. We validated our approach on a large hyperspectral dataset called TAIGA. A comprehensive qualitative and quantitative analysis of the results shows that the proposed method significantly outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-07-23T11:14:54Z)
• What matters when building vision-language models? [52.8539131958858]
  We develop Idefics2, an efficient foundational vision-language model with 8 billion parameters. Idefics2 achieves state-of-the-art performance within its size category across various multimodal benchmarks. We release the model (base, instructed, and chat) along with the datasets created for its training.
  arXiv  Detail & Related papers  (2024-05-03T17:00:00Z)
• AIDE: An Automatic Data Engine for Object Detection in Autonomous Driving [68.73885845181242]
  We propose an Automatic Data Engine (AIDE) that automatically identifies issues, efficiently curates data, improves the model through auto-labeling, and verifies the model through generation of diverse scenarios. We further establish a benchmark for open-world detection on AV datasets to comprehensively evaluate various learning paradigms, demonstrating our method's superior performance at a reduced cost.
  arXiv  Detail & Related papers  (2024-03-26T04:27:56Z)
• MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining [73.81862342673894]
  Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks. transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks. We conduct multi-task supervised pretraining on the SAMRS dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection. Our models are finetuned on various RS downstream tasks, such as scene classification, horizontal and rotated object detection, semantic segmentation, and change detection.
  arXiv  Detail & Related papers  (2024-03-20T09:17:22Z)
• Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning [101.66860222415512]
  Multi-Task Diffusion Model (textscMTDiff) is a diffusion-based method that incorporates Transformer backbones and prompt learning for generative planning and data synthesis. For generative planning, we find textscMTDiff outperforms state-of-the-art algorithms across 50 tasks on Meta-World and 8 maps on Maze2D.
  arXiv  Detail & Related papers  (2023-05-29T05:20:38Z)
• Towards Efficient Task-Driven Model Reprogramming with Foundation Models [52.411508216448716]
  Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only support a small model due to the limited computational resources or efficiency considerations. This brings a critical challenge for the real-world application of foundation models: one has to transfer the knowledge of a foundation model to the downstream task.
  arXiv  Detail & Related papers  (2023-04-05T07:28:33Z)
• Towards Estimating Transferability using Hard Subsets [25.86053764521497]
  We propose HASTE, a new strategy to estimate the transferability of a source model to a particular target task using only a harder subset of target data. We show that HASTE can be used with any existing transferability metric to improve their reliability. Our experimental results across multiple source model architectures, target datasets, and transfer learning tasks show that HASTE modified metrics are consistently better or on par with the state of the art transferability metrics.
  arXiv  Detail & Related papers  (2023-01-17T14:50:18Z)
• Transfer learning for conflict and duplicate detection in software requirement pairs [0.5359378066251386]
  Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software development processes by automatically identifying conflicting and duplicate software requirement specifications. We design a novel transformers-based architecture, SR-BERT, which incorporates Sentence-BERT and Bi-encoders for the conflict and duplicate identification task.
  arXiv  Detail & Related papers  (2023-01-09T22:47:12Z)
• Prototype-guided Cross-task Knowledge Distillation for Large-scale Models [103.04711721343278]
  Cross-task knowledge distillation helps to train a small student model to obtain a competitive performance. We propose a Prototype-guided Cross-task Knowledge Distillation (ProC-KD) approach to transfer the intrinsic local-level object knowledge of a large-scale teacher network to various task scenarios.
  arXiv  Detail & Related papers  (2022-12-26T15:00:42Z)
• Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design [4.091593765662773]
  We present the first Deep Generative Model that simultaneously optimize for performance, feasibility, diversity, and target achievement. Methods are tested on a challenging multi-objective bicycle frame design problem with skewed, multimodal data of different datatypes.
  arXiv  Detail & Related papers  (2022-06-14T20:57:23Z)
• Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design [4.091593765662773]
  Design Target Achievement Index (DTAI) is a differentiable, tunable metric that scores a design's ability to achieve designer-specified minimum performance targets. We apply DTAI to a Performance-Augmented Diverse GAN (PaDGAN) and demonstrate superior generative performance compared to a set of baseline Deep Generative Models.
  arXiv  Detail & Related papers  (2022-05-06T04:14:34Z)

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.