Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

What is modal logic

Recent years have seen the rise of Deep Learning (DL) techniques applied to source code. Researchers have exploited DL to automate several development and maintenance tasks, such as writing commit messages, generating comments and detecting vulnerabilities among others. One of the long lasting dreams of applying DL to source code is the possibility to automate non-trivial coding activities. While some steps in this direction have been taken (e.g., learning how to fix bugs), there is still a glaring lack of empirical evidence on the types of code changes that can be learned and automatically applied by DL. Our goal is to make this first important step by quantitatively and qualitatively investigating the ability of a Neural Machine Translation (NMT) model to learn how to automatically apply code changes implemented by developers during pull requests. We train and experiment with the NMT model on a set of 236k pairs of code components before and after the implementation of the changes provided in the pull requests. We show that, when applied in a narrow enough context (i.e., small/medium-sized pairs of methods before/after the pull request changes), NMT can automatically replicate the changes implemented by developers during pull requests in up to 36% of the cases. Moreover, our qualitative analysis shows that the model is capable of learning and replicating a wide variety of meaningful code changes, especially refactorings and bug-fixing activities. Our results pave the way for novel research in the area of DL on code, such as the automatic learning and applications of refactoring.

On learning meaningful code changes via neural machine translation

On Learning Meaningful Code Changes via Neural Machine Translation

An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pace at which DL techniques have been adopted, it is difficult to distill the current successes, failures, and opportunities of the current research landscape. In an effort to bring clarity to this cross-cutting area of work, from its modern inception to the present, this paper presents a systematic literature review of research at the intersection of SE & DL. The review canvases work appearing in the most prominent SE and DL conferences and journals and spans 84 papers across 22 unique SE tasks. We center our analysis around the components of learning, a set of principles that govern the application of machine learning techniques (ML) to a given problem domain, discussing several aspects of the surveyed work at a granular level. The end result of our analysis is a research roadmap that both delineates the foundations of DL techniques applied to SE research, and likely areas of fertile exploration for the future.

A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research.

Duplicate Bug Detection is the problem of identifying whether a newly reported bug is a duplicate of an existing bug in the system and retrieving the original or similar bugs from the past. This is required to avoid costly rediscovery and redundant work. In typical software projects, the number of duplicate bugs reported may run into the order of thousands, making it expensive in terms of cost and time for manual intervention. This makes the problem of duplicate or similar bug detection an important one in Software Engineering domain. However, an automated solution for the same is not quite accurate yet in practice, in spite of many reported approaches using various machine learning techniques. In this work, we propose a retrieval and classification model using Siamese Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) for accurate detection and retrieval of duplicate and similar bugs. We report an accuracy close to 90% and recall rate close to 80%, which makes possible the practical use of such a system. We describe our model in detail along with related discussions from the Deep Learning domain. By presenting the detailed experimental results, we illustrate the effectiveness of the model in practical systems, including for repositories for which supervised training data is not available.

Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques

With dozens of popular programming languages used worldwide, the number of source code files of programs available online for public use is massive. However most blogs, forums or online Q&A websites have poor searchability for specific programming language source code. Naive thumb rules based on the file extension if any are invariably used for syntax highlighting, indentation and other ways to improve readability of the code by programming language editors. A more systematic way to identify the language in which a given source file was written would be of immense value. We believe that simple Bayesiam models would be adequate for this given the intrinsic syntactic structure of any programming language. In this paper, we present Bayesian learning models for correctly identifying the programming language in which a given piece of source code was written, with high probability. We have used 20000 source code files across 10 programming languages to train and test the model using the following Bayesian classifier models – Naive Bayes, Bayesian Network and Multinomial Naive Bayes. Lastly, we show a performance comparison among the three models in terms of classification accuracy on the test data.

Detecting Programming Language from Source Code Using Bayesian Learning Techniques

Real time organization pulse gathering and analysis using machine learning and artificial intelligence

Recent initiatives in "open data" have resulted in good quality, freely available, tabular datasets on the web. However, such datasets are fragmented and arbitrarily structured and are not of much utility in isolation. To address this, there are several "open knowledge" initiatives that aim to stitch together open data elements into semantically meaningful structures. But such efforts are met with unique challenges. We argue in this paper that knowledge aggregation can be of two kinds -- encyclopedic aggregation, which aims to elucidate, and utilitarian aggregation, which aims to create actionable knowledge elements. We also argue that utilitarian aggregation is a characteristically different problem from that of conventional efforts like Freebase or DBpedia that address encyclopedic aggregation. In addition, when it comes to utilitarian knowledge, we observe that openness is not a binary condition and instead there is a need to distinguish between knowledge that is "open-ended" and knowledge that is "open." We formalize the notion of open knowledge based on how much knowledge or control does the creator of the knowledge element have about its consumers. Based on these arguments, we propose an underlying knowledge representation framework for encoding open utilitarian knowledge.

Characterizing Utilitarian Aggregation of Open Knowledge

Publishing structured datasets openly, called "open data" is gaining momentum worldwide. Government of India has started an open data initiative with the launch of its own portal. In addition, open data in India is also published by private firms, research institutes and NGOs (Non - Governmental Organizations). Although structured, the datasets do not comply to any a priori defined schema and semantic elements are fragmented across the disparate datasets. We are working on a project in which we aim to semantically aggregate Indian open data. This will help users to browse open data related to an entity and get a one stop entry to all the aspects of that entity. In this paper we discuss a survey conducted as part of this project to get a fair idea of the open data scene in India. This survey helped us to categorize the open data which makes the modeling easier.

A survey of Indian open data

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