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  1. Book ; Online: The Adverse Effects of Code Duplication in Machine Learning Models of Code

    Allamanis, Miltiadis

    2018  

    Abstract: The field of big code relies on mining large corpora of code to perform some learning task. A significant threat to this approach has been recently identified by Lopes et al. (2017) who found a large amount of near-duplicate code on GitHub. However, the ... ...

    Abstract The field of big code relies on mining large corpora of code to perform some learning task. A significant threat to this approach has been recently identified by Lopes et al. (2017) who found a large amount of near-duplicate code on GitHub. However, the impact of code duplication has not been noticed by researchers devising machine learning models for source code. In this article, we study the effect of code duplication to machine learning models showing that reported metrics are sometimes inflated by up to 100% when testing on duplicated code corpora compared to the performance on de-duplicated corpora which more accurately represent how machine learning models of code are used by software engineers. We present an "errata" for widely used datasets, list best practices for collecting code corpora and evaluating machine learning models on them, and release tools to help the community avoid this problem in future research.
    Keywords Computer Science - Software Engineering ; Computer Science - Machine Learning
    Subject code 005
    Publishing date 2018-12-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: JEMMA

    Karmakar, Anjan / Allamanis, Miltiadis / Robbes, Romain

    An Extensible Java Dataset for ML4Code Applications

    2022  

    Abstract: Machine Learning for Source Code (ML4Code) is an active research field in which extensive experimentation is needed to discover how to best use source code's richly structured information. With this in mind, we introduce JEMMA, an Extensible Java Dataset ...

    Abstract Machine Learning for Source Code (ML4Code) is an active research field in which extensive experimentation is needed to discover how to best use source code's richly structured information. With this in mind, we introduce JEMMA, an Extensible Java Dataset for ML4Code Applications, which is a large-scale, diverse, and high-quality dataset targeted at ML4Code. Our goal with JEMMA is to lower the barrier to entry in ML4Code by providing the building blocks to experiment with source code models and tasks. JEMMA comes with a considerable amount of pre-processed information such as metadata, representations (e.g., code tokens, ASTs, graphs), and several properties (e.g., metrics, static analysis results) for 50,000 Java projects from the 50KC dataset, with over 1.2 million classes and over 8 million methods. JEMMA is also extensible allowing users to add new properties and representations to the dataset, and evaluate tasks on them. Thus, JEMMA becomes a workbench that researchers can use to experiment with novel representations and tasks operating on source code. To demonstrate the utility of the dataset, we also report results from two empirical studies on our data, ultimately showing that significant work lies ahead in the design of context-aware source code models that can reason over a broader network of source code entities in a software project, the very task that JEMMA is designed to help with.
    Keywords Computer Science - Software Engineering ; Computer Science - Machine Learning
    Subject code 005
    Publishing date 2022-12-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: AdaptivePaste

    Liu, Xiaoyu / Jang, Jinu / Sundaresan, Neel / Allamanis, Miltiadis / Svyatkovskiy, Alexey

    Code Adaptation through Learning Semantics-aware Variable Usage Representations

    2022  

    Abstract: In software development, it is common for programmers to copy-paste or port code snippets and then adapt them to their use case. This scenario motivates the code adaptation task -- a variant of program repair which aims to adapt variable identifiers in a ...

    Abstract In software development, it is common for programmers to copy-paste or port code snippets and then adapt them to their use case. This scenario motivates the code adaptation task -- a variant of program repair which aims to adapt variable identifiers in a pasted snippet of code to the surrounding, preexisting source code. However, no existing approach has been shown to effectively address this task. In this paper, we introduce AdaptivePaste, a learning-based approach to source code adaptation, based on transformers and a dedicated dataflow-aware deobfuscation pre-training task to learn meaningful representations of variable usage patterns. We evaluate AdaptivePaste on a dataset of code snippets in Python. Results suggest that our model can learn to adapt source code with 79.8% accuracy. To evaluate how valuable is AdaptivePaste in practice, we perform a user study with 10 Python developers on a hundred real-world copy-paste instances. The results show that AdaptivePaste reduces the dwell time to nearly half the time it takes for manual code adaptation, and helps to avoid bugs. In addition, we utilize the participant feedback to identify potential avenues for improvement of AdaptivePaste.
    Keywords Computer Science - Software Engineering ; Computer Science - Computation and Language
    Subject code 005
    Publishing date 2022-05-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Self-Supervised Bug Detection and Repair

    Allamanis, Miltiadis / Jackson-Flux, Henry / Brockschmidt, Marc

    2021  

    Abstract: Machine learning-based program analyses have recently shown the promise of integrating formal and probabilistic reasoning towards aiding software development. However, in the absence of large annotated corpora, training these analyses is challenging. ... ...

    Abstract Machine learning-based program analyses have recently shown the promise of integrating formal and probabilistic reasoning towards aiding software development. However, in the absence of large annotated corpora, training these analyses is challenging. Towards addressing this, we present BugLab, an approach for self-supervised learning of bug detection and repair. BugLab co-trains two models: (1) a detector model that learns to detect and repair bugs in code, (2) a selector model that learns to create buggy code for the detector to use as training data. A Python implementation of BugLab improves by up to 30% upon baseline methods on a test dataset of 2374 real-life bugs and finds 19 previously unknown bugs in open-source software.

    Comment: Published in NeurIPS 2021
    Keywords Computer Science - Machine Learning ; Computer Science - Software Engineering
    Publishing date 2021-05-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Copy that! Editing Sequences by Copying Spans

    Panthaplackel, Sheena / Allamanis, Miltiadis / Brockschmidt, Marc

    2020  

    Abstract: Neural sequence-to-sequence models are finding increasing use in editing of documents, for example in correcting a text document or repairing source code. In this paper, we argue that common seq2seq models (with a facility to copy single tokens) are not ... ...

    Abstract Neural sequence-to-sequence models are finding increasing use in editing of documents, for example in correcting a text document or repairing source code. In this paper, we argue that common seq2seq models (with a facility to copy single tokens) are not a natural fit for such tasks, as they have to explicitly copy each unchanged token. We present an extension of seq2seq models capable of copying entire spans of the input to the output in one step, greatly reducing the number of decisions required during inference. This extension means that there are now many ways of generating the same output, which we handle by deriving a new objective for training and a variation of beam search for inference that explicitly handles this problem. In our experiments on a range of editing tasks of natural language and source code, we show that our new model consistently outperforms simpler baselines.

    Comment: Published in AAAI 2021
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-06-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Learning to Encode and Classify Test Executions

    Tsimpourlas, Foivos / Rajan, Ajitha / Allamanis, Miltiadis

    2020  

    Abstract: The challenge of automatically determining the correctness of test executions is referred to as the test oracle problem and is one of the key remaining issues for automated testing. The goal in this paper is to solve the test oracle problem in a way that ...

    Abstract The challenge of automatically determining the correctness of test executions is referred to as the test oracle problem and is one of the key remaining issues for automated testing. The goal in this paper is to solve the test oracle problem in a way that is general, scalable and accurate. To achieve this, we use supervised learning over test execution traces. We label a small fraction of the execution traces with their verdict of pass or fail. We use the labelled traces to train a neural network (NN) model to learn to distinguish runtime patterns for passing versus failing executions for a given program. Our approach for building this NN model involves the following steps, 1. Instrument the program to record execution traces as sequences of method invocations and global state, 2. Label a small fraction of the execution traces with their verdicts, 3. Designing a NN component that embeds information in execution traces to fixed length vectors, 4. Design a NN model that uses the trace information for classification, 5. Evaluate the inferred classification model on unseen execution traces from the program. We evaluate our approach using case studies from different application domains: 1. Module from Ethereum Blockchain, 2. Module from PyTorch deep learning framework, 3. Microsoft SEAL encryption library components, 4. Sed stream editor, 5. Value pointer library and 6. Nine network protocols from Linux packet identifier, L7-Filter. We found the classification models for all subject programs resulted in high precision, recall and specificity, over 95%, while only training with an average 9% of the total traces. Our experiments show that the proposed neural network model is highly effective as a test oracle and is able to learn runtime patterns to distinguish passing and failing test executions for systems and tests from different application domains.
    Keywords Computer Science - Software Engineering ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2020-01-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Is Surprisal in Issue Trackers Actionable?

    Caddy, James / Wagner, Markus / Treude, Christoph / Barr, Earl T. / Allamanis, Miltiadis

    2022  

    Abstract: Background. From information theory, surprisal is a measurement of how unexpected an event is. Statistical language models provide a probabilistic approximation of natural languages, and because surprisal is constructed with the probability of an event ... ...

    Abstract Background. From information theory, surprisal is a measurement of how unexpected an event is. Statistical language models provide a probabilistic approximation of natural languages, and because surprisal is constructed with the probability of an event occuring, it is therefore possible to determine the surprisal associated with English sentences. The issues and pull requests of software repository issue trackers give insight into the development process and likely contain the surprising events of this process. Objective. Prior works have identified that unusual events in software repositories are of interest to developers, and use simple code metrics-based methods for detecting them. In this study we will propose a new method for unusual event detection in software repositories using surprisal. With the ability to find surprising issues and pull requests, we intend to further analyse them to determine if they actually hold importance in a repository, or if they pose a significant challenge to address. If it is possible to find bad surprises early, or before they cause additional troubles, it is plausible that effort, cost and time will be saved as a result. Method. After extracting the issues and pull requests from 5000 of the most popular software repositories on GitHub, we will train a language model to represent these issues. We will measure their perceived importance in the repository, measure their resolution difficulty using several analogues, measure the surprisal of each, and finally generate inferential statistics to describe any correlations.

    Comment: 8 pages, 1 figure. Submitted to 2022 International Conference on Mining Software Repositories Registered Reports track
    Keywords Computer Science - Computation and Language ; Computer Science - Software Engineering ; H.3.3 ; I.2.7
    Subject code 005 ; 303
    Publishing date 2022-04-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: NS3

    Arakelyan, Shushan / Hakhverdyan, Anna / Allamanis, Miltiadis / Hauser, Christophe / Garcia, Luis / Ren, Xiang

    Neuro-Symbolic Semantic Code Search

    2022  

    Abstract: Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are ... ...

    Abstract Semantic code search is the task of retrieving a code snippet given a textual description of its functionality. Recent work has been focused on using similarity metrics between neural embeddings of text and code. However, current language models are known to struggle with longer, compositional text, and multi-step reasoning. To overcome this limitation, we propose supplementing the query sentence with a layout of its semantic structure. The semantic layout is used to break down the final reasoning decision into a series of lower-level decisions. We use a Neural Module Network architecture to implement this idea. We compare our model - NS3 (Neuro-Symbolic Semantic Search) - to a number of baselines, including state-of-the-art semantic code retrieval methods, and evaluate on two datasets - CodeSearchNet and Code Search and Question Answering. We demonstrate that our approach results in more precise code retrieval, and we study the effectiveness of our modular design when handling compositional queries.
    Keywords Computer Science - Machine Learning
    Subject code 006 ; 401
    Publishing date 2022-05-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Program Synthesis and Semantic Parsing with Learned Code Idioms

    Shin, Richard / Allamanis, Miltiadis / Brockschmidt, Marc / Polozov, Oleksandr

    2019  

    Abstract: Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time. In this work, we present ...

    Abstract Program synthesis of general-purpose source code from natural language specifications is challenging due to the need to reason about high-level patterns in the target program and low-level implementation details at the same time. In this work, we present PATOIS, a system that allows a neural program synthesizer to explicitly interleave high-level and low-level reasoning at every generation step. It accomplishes this by automatically mining common code idioms from a given corpus, incorporating them into the underlying language for neural synthesis, and training a tree-based neural synthesizer to use these idioms during code generation. We evaluate PATOIS on two complex semantic parsing datasets and show that using learned code idioms improves the synthesizer's accuracy.

    Comment: 33rd Conference on Neural Information Processing Systems (NeurIPS) 2019. 13 pages total, 9 pages of main text
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Programming Languages ; Statistics - Machine Learning
    Subject code 004
    Publishing date 2019-06-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: CoRGi

    Kim, Jooyeon / Lamb, Angus / Woodhead, Simon / Jones, Simon Peyton / Zheng, Cheng / Allamanis, Miltiadis

    Content-Rich Graph Neural Networks with Attention

    2021  

    Abstract: Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges). However, such projections often miss important and rich information. For example, in graph representations used in missing value ... ...

    Abstract Graph representations of a target domain often project it to a set of entities (nodes) and their relations (edges). However, such projections often miss important and rich information. For example, in graph representations used in missing value imputation, items - represented as nodes - may contain rich textual information. However, when processing graphs with graph neural networks (GNN), such information is either ignored or summarized into a single vector representation used to initialize the GNN. Towards addressing this, we present CoRGi, a GNN that considers the rich data within nodes in the context of their neighbors. This is achieved by endowing CoRGi's message passing with a personalized attention mechanism over the content of each node. This way, CoRGi assigns user-item-specific attention scores with respect to the words that appear in an item's content. We evaluate CoRGi on two edge-value prediction tasks and show that CoRGi is better at making edge-value predictions over existing methods, especially on sparse regions of the graph.
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2021-10-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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