SE3M: A Model for Software Effort Estimation Using Pre-trained Embedding Models

• URL: http://arxiv.org/abs/2006.16831v1
• Date: Tue, 30 Jun 2020 14:15:38 GMT
• Title: SE3M: A Model for Software Effort Estimation Using Pre-trained Embedding Models
• Authors: Eliane M. De Bortoli F\'avero and Dalcimar Casanova and Andrey Ricardo Pimentel
• Abstract summary: This paper proposes to evaluate the effectiveness of pre-trained embeddings models. Generic pre-trained models for both approaches went through a fine-tuning process. Results were very promising, realizing that pre-trained models can be used to estimate software effort based only on requirements texts.
• Score: 0.8287206589886881
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Estimating effort based on requirement texts presents many challenges, especially in obtaining viable features to infer effort. Aiming to explore a more effective technique for representing textual requirements to infer effort estimates by analogy, this paper proposes to evaluate the effectiveness of pre-trained embeddings models. For this, two embeddings approach, context-less and contextualized models are used. Generic pre-trained models for both approaches went through a fine-tuning process. The generated models were used as input in the applied deep learning architecture, with linear output. The results were very promising, realizing that pre-trained incorporation models can be used to estimate software effort based only on requirements texts. We highlight the results obtained to apply the pre-trained BERT model with fine-tuning in a single project repository, whose value is the Mean Absolute Error (MAE) is 4.25 and the standard deviation of only 0.17, which represents a result very positive when compared to similar works. The main advantages of the proposed estimation method are reliability, the possibility of generalization, speed, and low computational cost provided by the fine-tuning process, and the possibility to infer new or existing requirements.

Related papers

• Data Shapley in One Training Run [88.59484417202454]
  Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts. Existing approaches require re-training models on different data subsets, which is computationally intensive. This paper introduces In-Run Data Shapley, which addresses these limitations by offering scalable data attribution for a target model of interest.
  arXiv  Detail & Related papers  (2024-06-16T17:09:24Z)
• Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction [50.62245481416744]
  We present the first benchmark that simulates the evaluation of open information extraction models in the real world. We design and annotate a large-scale testbed in which each example is a knowledge-invariant clique. By further elaborating the robustness metric, a model is judged to be robust if its performance is consistently accurate on the overall cliques.
  arXiv  Detail & Related papers  (2023-05-23T12:05:09Z)
• fairml: A Statistician's Take on Fair Machine Learning Modelling [0.0]
  We describe the fairml package which implements our previous work (Scutari, Panero, and Proissl 2022) and related models in the literature. fairml is designed around classical statistical models and penalised regression results. The constraint used to enforce fairness is to model estimation, making it possible to mix-and-match the desired model family and fairness definition for each application.
  arXiv  Detail & Related papers  (2023-05-03T09:59:53Z)
• Evaluating Representations with Readout Model Switching [19.907607374144167]
  In this paper, we propose to use the Minimum Description Length (MDL) principle to devise an evaluation metric. We design a hybrid discrete and continuous-valued model space for the readout models and employ a switching strategy to combine their predictions. The proposed metric can be efficiently computed with an online method and we present results for pre-trained vision encoders of various architectures.
  arXiv  Detail & Related papers  (2023-02-19T14:08:01Z)
• Post-Selection Confidence Bounds for Prediction Performance [2.28438857884398]
  In machine learning, the selection of a promising model from a potentially large number of competing models and the assessment of its generalization performance are critical tasks. We propose an algorithm how to compute valid lower confidence bounds for multiple models that have been selected based on their prediction performances in the evaluation set.
  arXiv  Detail & Related papers  (2022-10-24T13:28:43Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Improving Pre-trained Language Model Fine-tuning with Noise Stability Regularization [94.4409074435894]
  We propose a novel and effective fine-tuning framework, named Layerwise Noise Stability Regularization (LNSR) Specifically, we propose to inject the standard Gaussian noise and regularize hidden representations of the fine-tuned model. We demonstrate the advantages of the proposed method over other state-of-the-art algorithms including L2-SP, Mixout and SMART.
  arXiv  Detail & Related papers  (2022-06-12T04:42:49Z)
• Self-Training of Handwritten Word Recognition for Synthetic-to-Real Adaptation [4.111899441919165]
  We propose a self-training approach to train a Handwritten Text Recognition model. The proposed training scheme uses an initial model trained on synthetic data to make predictions for the unlabeled target dataset. We evaluate the proposed method on four widely used benchmark datasets and show its effectiveness on closing the gap to a model trained in a fully-supervised manner.
  arXiv  Detail & Related papers  (2022-06-07T09:43:25Z)
• End-to-End Weak Supervision [15.125993628007972]
  We propose an end-to-end approach for directly learning the downstream model. We show improved performance over prior work in terms of end model performance on downstream test sets.
  arXiv  Detail & Related papers  (2021-07-05T19:10:11Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)
• Meta-Learned Confidence for Few-shot Learning [60.6086305523402]
  A popular transductive inference technique for few-shot metric-based approaches, is to update the prototype of each class with the mean of the most confident query examples. We propose to meta-learn the confidence for each query sample, to assign optimal weights to unlabeled queries. We validate our few-shot learning model with meta-learned confidence on four benchmark datasets.
  arXiv  Detail & Related papers  (2020-02-27T10:22:17Z)

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.