Type systems are responsible for assigning types to terms in programs. That way, they enforce the actions that can be taken and can, consequently, detect type errors during compilation. However, while they are able to flag the existence of an error, they often fail to pinpoint its cause or provide a helpful error message. Thus, without adequate support, debugging this kind of errors can take a considerable amount of effort. Recently, neural network models have been developed that are able to understand programming languages and perform several downstream tasks. We argue that type error debugging can be enhanced by taking advantage of this deeper understanding of the language’s structure. In this paper, we present a technique that leverages GPT-3’s capabilities to automatically fix type errors in OCaml programs. We perform multiple source code analysis tasks to produce useful prompts that are then provided to GPT-3 to generate potential patches. Our publicly available tool, Mentat, supports multiple modes and was validated on an existing public dataset with thousands of OCaml programs. We automatically validate successful repairs by using Quickcheck to verify which generated patches produce the same output as the user-intended fixed version, achieving a 39% repair rate. In a comparative study, Mentat outperformed two other techniques in automatically fixing ill-typed OCaml programs.

Recent studies show that many real-world software faults are due to slight modifications (mutations) to the program. Thus, analyzing transformations made by a developer and associating them with well-known mutation operators can help pinpoint and repair the root cause of failures. This paper proposes a mutation operator inference technique: given the original program and one of its subsequent forms, it infers which mutation operators would transform the original and produce such a version. Moreover, we implemented this technique as a tool called Morpheus, which analyzes faulty Java programs. We have also validated both the technique and tool by analyzing a repository with 1753 modifications for 20 different programs, successfully inferring mutation operators 78% of times. Furthermore, we also show that several program versions result from not just a single mutation operator but multiple ones. In the end, we resort to real-world case studies to demonstrate the advantages of this approach regarding program repair.

Cloud Computing is ranked as one of the most impacting technologies in present times. It represents a great change in how companies organize their budget, allowing then to save money otherwise spent in building and maintaining in-house data centers. If that were the only gain in the adoption of Cloud Computing technologies, it would be already a tremendous saving in costs, but Cloud solutions can provide users with furthermore services and options. With that in mind, what is the real impact of cloud computing to companies solutions and services? This paper conducts a systematic mapping in order to depict the economical and other impacts of cloud computing in companies and solution, analyzing aspects that go beyond technological questions.

This paper presents a study of the runtime, memory usage and energy consumption of twenty seven well-known software languages. We monitor the performance of such languages using ten different programming problems, expressed in each of the languages. Our results show interesting findings, such as, slower/faster languages consuming less/more energy, and how memory usage influences energy consumption. Finally, we show how to use our results to provide software engineers support to decide which language to use when energy efficiency is a concern.