Although source code programs are commonly written as textual information, they enclose syntactic and semantic information that is usually represented as graphs. This information is used for many different purposes, such as static program analysis, advanced code search, coding guideline checking, software metrics computation, and extraction of semantic and syntactic information to create predictive models. Most of the existing systems that provide these kinds of services are designed ad hoc for the particular purpose they are aimed at. For this reason, we created ProgQuery, a platform to allow users to write their own Java program analyses in a declarative fashion, using graph representations. We modify the Java compiler to compute seven syntactic and semantic representations, and store them in a Neo4j graph database. Such representations are overlaid, meaning that syntactic and semantic nodes of the different graphs are interconnected to allow combining different kinds of information in the queries/analyses. We evaluate ProgQuery and compare it to the related systems. Our platform outperforms the other systems in analysis time, and scales better to program sizes and analysis complexity. Moreover, the queries coded show that ProgQuery is more expressive than the other approaches. The additional information stored by ProgQuery increases the database size and associated insertion time, but these increases are significantly lower than the query/analysis performance gains obtained.

An emerging research topic called big code has recently appeared. Big code is based on the idea that open source code repositories can be used to create new kind of programming tools and services to improve software reliability and construction. We discuss different fields of application of big code, and the key issues to implement tools aimed at improving software construction following this approach. We describe the existing works that have already used this idea to build tools for vulnerability detection, software deobfuscation, automatic code completion for API usage, and efficient querying using detailed source-code information. Then, we propose different fields of application and the key issues found. We identify eight different fields where big code may be applied, and describe different examples for each field. We also detect seven different issues that must be tackled when creating tools based on the big code approach.

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We propose a new approach to recommend scientific literature, a domain in which the efficient organization and search of information is crucial. The proposed system relies on the hypothesis that two scientific articles are semantically related if they are co-cited more frequently than they would be by pure chance. This relationship can be quantified by the probability of co-citation, obtained from a null model that statistically defines what we consider pure chance. Looking for article pairs that minimize this probability, the system is able to recommend a ranking of articles in response to a given article. This system is included in the co-occurrence paradigm of the field. More specifically, it is based on co-cites so it can produce recommendations more focused on relatedness than on similarity. Evaluation has been performed on the ACL Anthology collection and on the DBLP dataset, and a new corpus has been compiled to evaluate the capacity of the proposal to find relationships beyond similarity. Results show that the system is able to provide, not only articles similar to the submitted one, but also articles presenting other kind of relations, thus providing diversity, i.e. connections to new topics.

Parsers are used in different software development scenarios such as compiler construction, data format processing, machine-level translation, and natural language processing. Due to the widespread usage of parsers, there exist different tools aimed at automizing their generation. Two of the most common parser generation tools are the classic Lex/Yacc and ANTLR. Even though ANTLR provides more advanced features, Lex/Yacc is still the preferred choice in many university courses. There exist different qualitative comparisons of the features provided by both approaches, but no study evaluates empirical features such as language implementor productivity and tool simplicity, intuitiveness, and maintainability. In this article, we present such an empirical study by conducting an experiment with undergraduate students of a Software Engineering degree. Two random groups of students implement the same language using a different parser generator, and we statistically compare their performance with different measures. Under the context of the academic study conducted, ANTLR has shown significant differences for most of the empirical features measured.