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Evaluation Methodologies for Code Learning Tasks
TL;DR: In this paper, a time-segmented evaluation methodology is proposed to evaluate code-comment pairs with timestamps to train and evaluate several recent code learning ML models for the comment generation and method naming tasks.
Abstract: There has been a growing interest in developing machine learning (ML) models for code learning tasks, e.g., comment generation and method naming. Despite substantial increase in the effectiveness of ML models, the evaluation methodologies, i.e., the way people split datasets into training, validation, and testing sets, were not well designed. Specifically, no prior work on the aforementioned topics considered the timestamps of code and comments during evaluation (e.g., examples in the testing set might be from 2010 and examples from the training set might be from 2020). This may lead to evaluations that are inconsistent with the intended use cases of the ML models. In this paper, we formalize a novel time-segmented evaluation methodology, as well as the two methodologies commonly used in the literature: mixed-project and cross-project. We argue that time-segmented methodology is the most realistic. We also describe various use cases of ML models and provide a guideline for using methodologies to evaluate each use case. To assess the impact of methodologies, we collect a dataset of code-comment pairs with timestamps to train and evaluate several recent code learning ML models for the comment generation and method naming tasks. Our results show that different methodologies can lead to conflicting and inconsistent results. We invite the community to adopt the time-segmented evaluation methodology.
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01 Jan 2022
TL;DR: In this article , the authors propose to generate a concise natural language description of the solution by synthesizing relevant content within the discussion, which encompasses both natural language and source code, and design two systems for generating a description during an ongoing discussion by classifying when sufficient context for performing the task emerges.
Abstract: When a software bug is reported, developers engage in a discussion to collaboratively resolve it. While the solution is likely formulated within the discussion, it is often buried in a large amount of text, making it difficult to comprehend and delaying its implementation. To expedite bug resolution, we propose generating a concise natural language description of the solution by synthesizing relevant content within the discussion, which encompasses both natural language and source code. We build a corpus for this task using a novel technique for obtaining noisy supervision from repository changes linked to bug reports, with which we establish benchmarks. We also design two systems for generating a description during an ongoing discussion by classifying when sufficient context for performing the task emerges in real-time. With automated and human evaluation, we find this task to form an ideal testbed for complex reasoning in long, bimodal dialogue context.
Posted Content•
TL;DR: In this paper, the authors propose to generate a concise natural language description of the solution by synthesizing relevant content within the discussion, which encompasses both natural language and source code, and determine when sufficient context about the solution emerges in real-time.
Abstract: When a software bug is reported, developers engage in a discussion to collaboratively resolve it. While the solution is likely formulated within the discussion, it is often buried in a large amount of text, making it difficult to comprehend, which delays its implementation. To expedite bug resolution, we propose generating a concise natural language description of the solution by synthesizing relevant content within the discussion, which encompasses both natural language and source code. Furthermore, to support generating an informative description during an ongoing discussion, we propose a secondary task of determining when sufficient context about the solution emerges in real-time. We construct a dataset for these tasks with a novel technique for obtaining noisy supervision from repository changes linked to bug reports. We establish baselines for generating solution descriptions, and develop a classifier which makes a prediction following each new utterance on whether or not the necessary context for performing generation is available. Through automated and human evaluation, we find these tasks to form an ideal testbed for complex reasoning in long, bimodal dialogue context.
References
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Proceedings Article•
12 Jun 2017TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Abstract: The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.
52,856 citations
IBM1
TL;DR: This paper proposed a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run.
Abstract: Human evaluations of machine translation are extensive but expensive. Human evaluations can take months to finish and involve human labor that can not be reused. We propose a method of automatic machine translation evaluation that is quick, inexpensive, and language-independent, that correlates highly with human evaluation, and that has little marginal cost per run. We present this method as an automated understudy to skilled human judges which substitutes for them when there is need for quick or frequent evaluations.
21,126 citations
TL;DR: A new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains, and implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space.
Abstract: Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. This GNN model, which can directly process most of the practically useful types of graphs, e.g., acyclic, cyclic, directed, and undirected, implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space. A supervised learning algorithm is derived to estimate the parameters of the proposed GNN model. The computational cost of the proposed algorithm is also considered. Some experimental results are shown to validate the proposed learning algorithm, and to demonstrate its generalization capabilities.
5,701 citations
Proceedings Article•
01 Jun 2005TL;DR: METEOR is described, an automatic metric for machine translation evaluation that is based on a generalized concept of unigram matching between the machineproduced translation and human-produced reference translations and can be easily extended to include more advanced matching strategies.
Abstract: We describe METEOR, an automatic metric for machine translation evaluation that is based on a generalized concept of unigram matching between the machineproduced translation and human-produced reference translations. Unigrams can be matched based on their surface forms, stemmed forms, and meanings; furthermore, METEOR can be easily extended to include more advanced matching strategies. Once all generalized unigram matches between the two strings have been found, METEOR computes a score for this matching using a combination of unigram-precision, unigram-recall, and a measure of fragmentation that is designed to directly capture how well-ordered the matched words in the machine translation are in relation to the reference. We evaluate METEOR by measuring the correlation between the metric scores and human judgments of translation quality. We compute the Pearson R correlation value between its scores and human quality assessments of the LDC TIDES 2003 Arabic-to-English and Chinese-to-English datasets. We perform segment-bysegment correlation, and show that METEOR gets an R correlation value of 0.347 on the Arabic data and 0.331 on the Chinese data. This is shown to be an improvement on using simply unigramprecision, unigram-recall and their harmonic F1 combination. We also perform experiments to show the relative contributions of the various mapping modules.
3,911 citations
03 Mar 2021
TL;DR: The authors take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? They provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.
Abstract: The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.
1,395 citations