Moving from Cross-Project Defect Prediction to Heterogeneous Defect Prediction: A Partial Replication Study

• URL: http://arxiv.org/abs/2103.03490v1
• Date: Fri, 5 Mar 2021 06:29:45 GMT
• Title: Moving from Cross-Project Defect Prediction to Heterogeneous Defect Prediction: A Partial Replication Study
• Authors: Hadi Jahanshahi, Mucahit Cevik, Ay\c{s}e Ba\c{s}ar
• Abstract summary: Earlier studies often used machine learning techniques to build, validate, and improve bug prediction models. Knowledge coming from those models will not be overlapping to a target project if no sufficient metrics have been collected in the source projects. We systematically integrated Heterogeneous Defect Prediction (HDP) by replicating and validating the obtained results. Our results shed light on the infeasibility of many cases for the HDP algorithm due to its sensitivity to the parameter selection.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Software defect prediction heavily relies on the metrics collected from software projects. Earlier studies often used machine learning techniques to build, validate, and improve bug prediction models using either a set of metrics collected within a project or across different projects. However, techniques applied and conclusions derived by those models are restricted by how identical those metrics are. Knowledge coming from those models will not be extensible to a target project if no sufficient overlapping metrics have been collected in the source projects. To explore the feasibility of transferring knowledge across projects without common labeled metrics, we systematically integrated Heterogeneous Defect Prediction (HDP) by replicating and validating the obtained results. Our main goal is to extend prior research and explore the feasibility of HDP and finally to compare its performance with that of its predecessor, Cross-Project Defect Prediction. We construct an HDP model on different publicly available datasets. Moreover, we propose a new ensemble voting approach in the HDP context to utilize the predictive power of multiple available datasets. The result of our experiment is comparable to that of the original study. However, we also explored the feasibility of HDP in real cases. Our results shed light on the infeasibility of many cases for the HDP algorithm due to its sensitivity to the parameter selection. In general, our analysis gives a deep insight into why and how to perform transfer learning from one domain to another, and in particular, provides a set of guidelines to help researchers and practitioners to disseminate knowledge to the defect prediction domain.

Related papers

• The Impact of Defect (Re) Prediction on Software Testing [1.5869998695491834]
  Cross-project defect prediction (CPDP) aims to use data from external projects as historical data may not be available from the same project. A Bandit Algorithm (BA) based approach has been proposed in prior research to select the most suitable learning project. This study aims to improve the BA method to reduce defects overlooking, especially during the early testing stages.
  arXiv  Detail & Related papers  (2024-04-17T03:34:13Z)
• Explainable Software Defect Prediction from Cross Company Project Metrics Using Machine Learning [5.829545587965401]
  This study focuses on developing defect prediction models that apply various machine learning algorithms. One notable issue in existing defect prediction studies is the lack of transparency in the developed models.
  arXiv  Detail & Related papers  (2023-06-14T17:46:08Z)
• Think Twice: Measuring the Efficiency of Eliminating Prediction Shortcuts of Question Answering Models [3.9052860539161918]
  We propose a simple method for measuring a scale of models' reliance on any identified spurious feature. We assess the robustness towards a large set of known and newly found prediction biases for various pre-trained models and debiasing methods in Question Answering (QA) We find that while existing debiasing methods can mitigate reliance on a chosen spurious feature, the OOD performance gains of these methods can not be explained by mitigated reliance on biased features.
  arXiv  Detail & Related papers  (2023-05-11T14:35:00Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Defect Prediction Using Stylistic Metrics [2.286041284499166]
  This paper aims at analyzing the impact of stylistic metrics on both within-project and crossproject defect prediction. Experiment is conducted on 14 releases of 5 popular, open source projects.
  arXiv  Detail & Related papers  (2022-06-22T10:11:05Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Uncertainty Prediction for Machine Learning Models of Material Properties [0.0]
  Uncertainty in AI-based predictions of material properties is of immense importance for the success and reliability of AI applications in material science. We compare 3 different approaches to obtain such individual uncertainty, testing them on 12 ML-physical properties.
  arXiv  Detail & Related papers  (2021-07-16T16:33:55Z)
• Test-time Collective Prediction [73.74982509510961]
  Multiple parties in machine learning want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents, but may not be willing to release their data or model parameters. We explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model.
  arXiv  Detail & Related papers  (2021-06-22T18:29:58Z)
• NADS: Neural Architecture Distribution Search for Uncertainty Awareness [79.18710225716791]
  Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data. Existing OoD detection approaches are prone to errors and even sometimes assign higher likelihoods to OoD samples. We propose Neural Architecture Distribution Search (NADS) to identify common building blocks among all uncertainty-aware architectures.
  arXiv  Detail & Related papers  (2020-06-11T17:39:07Z)
• Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models [110.82452096672182]
  We propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data. We also introduce a novel metric for ambiguous problems, which is better suited to account for uncertainties.
  arXiv  Detail & Related papers  (2020-03-10T09:15:42Z)
• 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.