Leveraging Patterns and Interesting Anomalies through Scalable Semantic Analysis

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Smartphones, automobiles, medical devices, the banking system, the electrical grid: software runs many aspects of our world, and its reach increases every day. Superficially, we can see that there are differences between each of these applications, but how deep do those differences run? Is there a lot of commonality in its functionality? Are there patterns to the mistakes that people make when building it? Are there hidden gems in which someone has solved common problems in an innovative way? Such questions are beyond our current scientific and engineering abilities to answer on a large scale. Finding those commonalities, patterns, and gems would help us to learn from experience, support good approaches, and warn of bad approaches, based on evidence and not on wishful thinking. This would allow better software to be created, ultimately benefitting our society which depends on that software.Achieving this goal is not straightforward. Much data is already collected about software in the real world. Unfortunately, such data is based on textual details: while text-based data is good enough to allow search engines like Google to answer common questions (like finding a list of local restaurants), text-based data says nothing about the underlying meaning of the text. The behaviour of a software program is achieved by a computer performing a detailed set of instructions, written in a software programming language. While human languages are complex and ambiguous, software languages are necessarily unambiguous: the computer is dumb but faithful, doing exactly as it is told. In principle, we can understand the possible behaviours of software (called its semantics) by analyzing the text in which it is written (called source code). But we face challenges: industrial software tends to be really big; there is a lot of it; and we have no reliable means of comparing the semantics of one software program with those of another.We will address these challenges by tackling two key problems: we must analyze software semantics on a large-scale, overcoming the fundamental technical problems that limit the usefulness of traditional approaches; and we must compare and contrast the results of such analyses, also on a large-scale, in order to discover patterns and anomalies in the software semantics. We will develop techniques to permit the semantic exploration of huge amounts of software source code. We will start that exploration to demonstrate the value of our novel techniques. Our discoveries will lead to deeper understanding of the nature of software, enriching our scientific knowledge. Deeper knowledge of good and bad practices will lead to better software, by avoiding bad practices but enforcing good practices. Society, which depends ever more heavily on software, will benefit as a result of better software, which is produced at lower costs.