Semantic Source Code Models Using Identifier Embeddings

The emergence of online open source repositories in the recent years has led to an explosion in the volume of openly available source code, coupled with metadata that relate to a variety of software development activities. As an effect, in line with recent advances in machine learning research, software maintenance activities are switching from symbolic formal methods to data-driven methods. In this context, the rich semantics hidden in source code identifiers provide opportunities for building semantic representations of code which can assist tasks of code search and reuse. To this end, we deliver in the form of pretrained vector space models, distributed code representations for six popular programming languages, namely, Java, Python, PHP, C, C++, and C#. The models are produced using fastText, a state-of-the-art library for learning word representations. Each model is trained on data from a single programming language; the code mined for producing all models amounts to over 13.000 repositories. We indicate dissimilarities between natural language and source code, as well as variations in coding conventions in between the different programming languages we processed. We describe how these heterogeneities guided the data preprocessing decisions we took and the selection of the training parameters in the released models. Finally, we propose potential applications of the models and discuss limitations of the models.Comment: 16th International Conference on Mining Software Repositories (MSR 2019): Data Showcase Trac

Looking Over the Research Literature on Software Engineering from 2016 to 2018

This paper carries out a bibliometric analysis to detect (i) what is the most influential research on software engineering at the moment, (ii) where is being published that relevant research, (iii) what are the most commonly researched topics, (iv) and where is being undertaken that research (i.e., in which countries and institutions). For that, 6,365 software engineering articles, published from 2016 to 2018 on a variety of conferences and journals, are examined.This work has been funded by the Spanish Ministry of Science, Innovation, and Universities under Project DPI2016-77677-P, the Community of Madrid under Grant RoboCity2030-DIH-CM P2018/NMT-4331, and grant TIN2016-75850-R from the FEDER funds

Open Vocabulary Learning on Source Code with a Graph-Structured Cache

Machine learning models that take computer program source code as input typically use Natural Language Processing (NLP) techniques. However, a major challenge is that code is written using an open, rapidly changing vocabulary due to, e.g., the coinage of new variable and method names. Reasoning over such a vocabulary is not something for which most NLP methods are designed. We introduce a Graph-Structured Cache to address this problem; this cache contains a node for each new word the model encounters with edges connecting each word to its occurrences in the code. We find that combining this graph-structured cache strategy with recent Graph-Neural-Network-based models for supervised learning on code improves the models' performance on a code completion task and a variable naming task --- with over $100\%$ relative improvement on the latter --- at the cost of a moderate increase in computation time.Comment: Published in the International Conference on Machine Learning (ICML 2019), 13 page

500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow)

Deep learning methods are useful for high-dimensional data and are becoming widely used in many areas of software engineering. Deep learners utilizes extensive computational power and can take a long time to train-- making it difficult to widely validate and repeat and improve their results. Further, they are not the best solution in all domains. For example, recent results show that for finding related Stack Overflow posts, a tuned SVM performs similarly to a deep learner, but is significantly faster to train. This paper extends that recent result by clustering the dataset, then tuning very learners within each cluster. This approach is over 500 times faster than deep learning (and over 900 times faster if we use all the cores on a standard laptop computer). Significantly, this faster approach generates classifiers nearly as good (within 2\% F1 Score) as the much slower deep learning method. Hence we recommend this faster methods since it is much easier to reproduce and utilizes far fewer CPU resources. More generally, we recommend that before researchers release research results, that they compare their supposedly sophisticated methods against simpler alternatives (e.g applying simpler learners to build local models)

A Mocktail of Source Code Representations

Efficient representation of source code is essential for various software engineering tasks such as code search and code clone detection. One such technique for representing source code involves extracting paths from the AST and using a learning model to capture program properties. Code2vec is a commonly used path-based approach that uses an attention-based neural network to learn code embeddings which can then be used for various software engineering tasks. However, this approach uses only ASTs and does not leverage other graph structures such as Control Flow Graphs (CFG) and Program Dependency Graphs (PDG). Similarly, most recent approaches for representing source code still use AST and do not leverage semantic graph structures. Even though there exists an integrated graph approach (Code Property Graph) for representing source code, it has only been explored in the domain of software security. Moreover, it does not leverage the paths from the individual graphs. In our work, we extend the path-based approach code2vec to include semantic graphs, CFG, and PDG, along with AST, which is still largely unexplored in the domain of software engineering. We evaluate our approach on the task of MethodNaming using a custom C dataset of 730K methods collected from 16 C projects from GitHub. In comparison to code2vec, our approach improves the F1 Score by 11% on the full dataset and up to 100% with individual projects. We show that semantic features from the CFG and PDG paths are indeed helpful. We envision that looking at a mocktail of source code representations for various software engineering tasks can lay the foundation for a new line of research and a re-haul of existing research