REST services are nowadays being used to support many businesses, with most major companies exposing their services via REST interfaces (e.g., Google, Amazon, Instagram, and Slack). In this type of scenarios, heterogeneity is prevalent and software is sometimes exposed to unexpected conditions that may activate residual bugs, leading service operations to fail. Such failures may lead to financial or reputation losses (e.g., information disclosure). Although techniques and tools for assessing robustness have been thoroughly studied and applied to a large diversity of domains, REST services still lack practical approaches that specialize in robustness evaluation. In this paper, we present a tool (named bBOXRT) for performing robustness tests over REST services, solely based on minimal information expressed in their interface descriptions. We used bBOXRT to evaluate an heterogeneous set of 52 REST services that comprise 1,351 operations and fit in distinct categories (e.g., public, private, in-house). We were able to disclose several different types of robustness problems, including issues in services with strong reliability requirements and also a few security vulnerabilities. The results show that REST services are being deployed preserving software defects that harm service integration, and also carrying security vulnerabilities that can be exploited by malicious users.

Software systems are increasingly being used in business or mission critical scenarios, where the presence of certain types of software defects, i.e., bugs, may result in catastrophic consequences (e.g., financial losses or even the loss of human lives). To deploy systems in which we can rely on, it is vital to understand the types of defects that tend to affect such systems. This allows developers to take proper action, such as adapting the development process or redirecting testing efforts (e.g., using a certain set of testing techniques, or focusing on certain parts of the system). Orthogonal Defect Classification (ODC) has emerged as a popular method for classifying bugs, but it requires one or more experts to categorize each bug in a quite complex and time-consuming process. In this paper, we evaluate the use of machine learning algorithms (k-Nearest Neighbors, Support Vector Machines, Naïve Bayes, Nearest Centroid and Random Forest) for automatic classification of software defects using ODC, based on unstructured textual bug reports. Experimental results reveal the difficulties in automatically classifying certain ODC attributes solely using reports, but also suggest that the overall classification accuracy may be improved in most of the cases, if larger datasets are used.

NoSQL databases are increasingly used for storing and managing data in business-critical Big Data systems. The presence of software defects (i.e., bugs) in these databases can bring in severe consequences to the NoSQL services being offered, such as data loss or service unavailability. Thus, it is essential to understand the types of defects that frequently affect these databases, allowing developers take action in an informed manner (e.g., redirect testing efforts). In this paper, we use Orthogonal Defect Classification (ODC) to classify a total of 4096 software defects from three of the current top NoSQL databases: MongoDB, Cassandra, and HBase. The results show great similarity for the defects across the three different NoSQL systems and, at the same time, show the differences and heterogeneity regarding works carried out in other domains and types of applications, emphasizing the need for possessing such unique information. Our results expose the defect distributions in NoSQL databases, provide a foundation for selecting representative defects for NoSQL systems, and, overall, can be useful for developers for verifying and building more reliable NoSQL database systems.

Robustness is the degree to which a certain system or component can operate correctly in the presence of invalid inputs or stressful environmental conditions. With the increasing complexity and widespread use of computer systems, obtaining assurances regarding their robustness has become of vital importance. This survey discusses the state of the art on software robustness assessment, with emphasis on key aspects like types of systems being evaluated, assessment techniques used, the target of the techniques, the types of faults used, and how system behavior is classified. The survey concludes with the identification of gaps and open challenges related with robustness assessment.

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