Mutation testing is a fault-based testing criterion that is used to measure the quality of the test suites of software systems. Due to its inherent high computational cost, many studies were published in the last decades aiming at reducing computational cost and human-effort for the mutation analysis. One of the most promising areas is searching for Strongly Subsuming Higher-Order Mutants (SSHOMs), which are rare and harder to kill than their constituent first-order mutants (FOMs). Therefore, they are valuable especially because they can replace their FOMs without loss of effectiveness in the mutation testing process. One of the explanations for the SSHOMs to be harder to kill than their constituent FOMs is that the single faults (mutations) can partially mask one another, so that the combination of them is harder to detect than any of the individual faults. However, we did not find in the literature an investigation of the masking phenomenon. Therefore, the goal of this paper is to start filling this gap. More specifically, for a mutation to mask the other one, it is necessary firstly that the execution of a test case reaches all involved mutations. Therefore, we designed two complementary studies to accomplish our goal. Study #1 focuses on searching for Strongly-Subsuming Second-Order Mutants (SS2OMs) and then analyzes reaching characteristics of their constituent FOMs. We found that almost half of the SS2OMs constituent FOMs are not even reaching the other FOM. In Study #2, we designed a search strategy that considers a second-order mutant killed only if both of their mutations are reached by a failing test case execution. This strategy found much more SS2OMs than in the first study.

Mutation testing is a fault-based technique commonly used to evaluate the quality of test suites in software systems. It consists of introducing syntactical changes, called mutations, into source code and checking whether the test cases distinguish them. Since there are dozens of distinct mutation types, one of the most challenging problems is the high computational effort required to test the whole test suite against each mutant. Since mutation testing is proposed, researchers have presented techniques aiming at effort reduction in the phases of its process. This study focuses on the potential reduction in the number of mutants provided by a special set of mutants generated by the introduction of two syntactical changes (strongly subsuming second-order mutants). In this work, we exhaustively searched for those second-order mutants Our results show that they (i) are frequently generated by the "expression removal" mutation, (ii) are likely to be killed by the same test cases that kill their constituent mutants, and (iii) have the potential to reduce the number of mutants to be executed by about 22.3%.

GitHub repository issues can be “tagged” with labels to provide better understanding, organization, classification and to make information retrieval easier for both users and project managers. GitHub provides nine default labels and allows users to create, edit, and delete labels to fit the project maintainers' management goals. Such labels can, for example, help users to find open source projects that are open for new collaborators since they are able to search for the default label good first issuein GitHub's search engine. However, such a mechanism would be more powerful if the platform knew semantically similar customized labels and also reaches projects with them. In this study, we investigate two NBNE-based approaches and another based on Word2Vec algorithm to represent labels as embeddings (i.e., as vectors on a multidimensional space), so that semantically similar labels get closer. As a result, we found that Word2Vec is better indicated for this task, although it actually deserves further investigation.

Higher-order mutation has the potential for improving major drawbacks of traditional first-order mutation, such as by simulating more realistic faults or improving test-optimization techniques. Despite interest in studying promising higher-order mutants, such mutants are difficult to find due to the exponential search space of mutation combinations. State-of-the-art approaches rely on genetic search, which is often incomplete and expensive due to its stochastic nature. First, we propose a novel way of finding a complete set of higher-order mutants by using variational execution, a technique that can, in many cases, explore large search spaces completely and often efficiently. Second, we use the identified complete set of higher-order mutants to study their characteristics. Finally, we use the identified characteristics to design and evaluate a new search strategy, independent of variational execution, that is highly effective at finding higher-order mutants even in large codebases.

The increasing use of smartphones to access social media platforms opens a new wave of applications that explore sentiment analysis in the mobile environment. However, there are various existing sentiment analysis methods and it is unclear which of them are deployable in the mobile environment. This paper provides the first of a kind study in which we compare the performance of 17 sentence-level sentiment analysis methods in the mobile environment. To do that, we adapted these sentence-level methods to run on Android OS and then we measure their performance in terms of memory usage, CPU usage, and battery consumption. Our findings unveil sentence-level methods that require almost no adaptations and run relatively fast as well as methods that could not be deployed due to excessive use of memory. We hope our effort provides a guide to developers and researchers interested in exploring sentiment analysis as part of a mobile application and can help new applications to be executed without the dependency of a server-side API.