We provide a repository of 50,000 compilable Java projects. Each project in this dataset comes with references to all the dependencies required to compile it, the resulting bytecode, and the scripts with which the projects were built.

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Software Heritage is the largest existing public archive of software source code and accompanying development history. It spans more than five billion unique source code files and one billion unique commits, coming from more than 80 million software projects. These software artifacts were retrieved from major collaborative development platforms (e.g., GitHub, GitLab) and package repositories (e.g., PyPI, Debian, NPM), and stored in a uniform representation linking together source code files, directories, commits, and full snapshots of version control systems (VCS) repositories as observed by Software Heritage during periodic crawls. This dataset is unique in terms of accessibility and scale, and allows to explore a number of research questions on the long tail of public software development, instead of solely focusing on "most starred" repositories as it often happens.

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Learning from large corpora of source code ("Big Code") has seen increasing interest over the past few years. A first wave of work has focused on leveraging off-the-shelf methods from other machine learning fields such as natural language processing. While these techniques have succeeded in showing the feasibility of learning from code, and led to some initial practical solutions, they forego explicit use of known program semantics. In a range of recent work, we have tried to solve this issue by integrating deep learning techniques with program analysis methods in graphs. Graphs are a convenient, general formalism to model entities and their relationships, and are seeing increasing interest from machine learning researchers as well. In this talk, I present two applications of graph-based learning to understanding and generating programs and discuss a range of future work building on the success of this work.

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Choosing the appropriate Missing Data (MD) imputation technique for a given Software development effort estimation (SDEE) technique is not a trivial task. In fact, the impact of the MD imputation on the estimation output depends on the dataset and the SDEE technique used and there is no best imputation technique in all contexts. Thus, an attractive solution is to use more than one single imputation technique and combine their results for a final imputation outcome. This concept is called ensemble imputation and can help to significantly improve the estimation accuracy. This paper develops and evaluates a heterogeneous ensemble imputation whose members were the four single imputation techniques: K-Nearest Neighbors (KNN), Expectation Maximization (EM), Support Vector Regression (SVR), and Decision Trees (DT). The impact of the ensemble imputation was evaluated and compared with those of the four single imputation techniques on the accuracy measured in terms of the standardized accuracy criterion of four SDEE techniques: Case Based Reasoning (CBR), Multi-Layers Perceptron (MLP), Support Vector Regression (SVR) and Reduced Error Pruning Tree (REPTree). The Wilcoxon statistical test was also performed in order to assess whether the results are significant. All the empirical evaluations were carried out over the six datasets, namely, ISBSG, China, COCOMO81, Desharnais, Kemerer, and Miyazaki. Results show that the use of heterogeneous ensemble-based imputation instead single imputation significantly improved the accuracy of the four SDEE techniques. Indeed, the ensemble imputation technique was ranked either first or second in all contexts.

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Spoken language interfaces are the latest trend in human computer interaction. Users enjoy the newly found freedom but developers face an unfamiliar and daunting task. Creating reactive spoken language interfaces requires skills in natural language processing. We show how a developer can integrate a dialog component in a natural language processing system by means of software engineering methods. Our research project PARSE that aims at naturalistic end-user programming in spoken natural language serves as an example. We integrate a dialog component with PARSE without affecting its other components: We modularize the dialog management and introduce dialog acts that bundle a trigger for the dialog and the reaction of the system. We implemented three dialog acts to address the following issues: speech recognition uncertainties, coreference ambiguities, and incomplete conditionals.

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Continuous Integration and Continuous Delivery are established practices in modern agile software development. The DevOps movement adapted theses practices and places the deployment pipeline at its heart as one of the main requirements to automate the software development process and to deliver and operate software in a more robust way with higher quality.

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