Web Services like REST, GraphQL and RPC APIs are widely used in industry. They form the backends of modern Cloud Applications. In recent years, there has been an increase interest in the research community about fuzzing web services. However, there is no clear, common benchmark in the literature that can be used for comparing techniques and ease experimentation. Even if nowadays it is not so difficult to find web services on open-source repositories such as GitHub, quite a bit of work might be required to setup databases and authentication information (e.g., hashed passwords). Furthermore, how to start and stop the applications might vary greatly among the different frameworks (e.g., Spring and DropWizard) used to implement such services. For all these reasons, since 2017 we have created and maintained a corpus of web services called EMB, together with all the tooling and configurations needed to run software testing experiments. Originally, EMB was created for evaluating the fuzzer EvoMaster, but it can be (and has been) used by other tools/researchers as well. This paper discusses how EMB is designed and how its libraries can be used to run experiments on these APIs. An introductory video for EMB can be currently accessed at https://youtu.be/wJs34ATgLEw

Research in software testing often involves the development of software prototypes. Like any piece of software, there are challenges in the development, use and verification of such tools. However, some challenges are rather specific to this problem domain. For example, often these tools are developed by PhD students straight out of bachelor/master degrees, possibly lacking any industrial experience in software development. Prototype tools are used to carry out empirical studies, possibly studying different parameters of novel designed algorithms. Software scaffolding is needed to run large sets of experiments efficiently. Furthermore, when using AI-based techniques like evolutionary algorithms, care needs to be taken to deal with their randomness, which further complicates their verification. The aforementioned represent some of the challenges we have identified for this domain. In this paper, we report on our experience in building the open-source EvoMaster tool, which aims at system-level test case generation for enterprise applications. Many of the challenges we faced would be common to any researcher needing to build software testing tool prototypes. Therefore, one goal is that our shared experience here will boost the research community, by providing concrete solutions to many development challenges in the building of such kind of research prototypes. Ultimately, this will lead to increase the impact of scientific research on industrial practice.

RESTful web services are often used for building a wide variety of enterprise applications. The diversity and increased number of applications using RESTful APIs means that increasing amounts of resources are spent developing and testing these systems. Automation in test data generation provides a useful way of generating test data in a fast and efficient manner. However, automated test generation often results in large test suites that are hard to evaluate and investigate manually. This article proposes a taxonomy of the faults we have found using search-based software testing techniques applied on RESTful APIs. The taxonomy is a first step in understanding, analyzing, and ultimately fixing software faults in web services and enterprise applications. We propose to apply a density-based clustering algorithm to the test cases evolved during the search to allow a better separation between different groups of faults. This is needed to enable engineers to highlight and focus on the most serious faults. Tests were automatically generated for a set of eight case studies, seven open-source and one industrial. The test cases generated during the search are clustered based on the reported last executed line and based on the error messages returned, when such error messages were available. The tests were manually evaluated to determine their root causes and to obtain additional information. The article presents a taxonomy of the faults found based on the manual analysis of 415 faults in the eight case studies and proposes a method to support the classification using clustering of the resulting test cases.

Abstract Nowadays, RESTful web services are widely used for building enterprise applications. REST is not a protocol, but rather it defines a set of guidelines on how to design APIs to access and manipulate resources using HTTP over a network. In this paper, we propose an enhanced search-based method for automated system test generation for RESTful web services, by exploiting domain knowledge on the handling of HTTP resources. The proposed techniques use domain knowledge specific to RESTful web services and a set of effective templates to structure test actions (i.e., ordered sequences of HTTP calls) within an individual in the evolutionary search. The action templates are developed based on the semantics of HTTP methods and are used to manipulate the web services’ resources. In addition, we propose five novel sampling strategies with four sampling methods (i.e., resource-based sampling) for the test cases that can use one or more of these templates. The strategies are further supported with a set of new, specialized mutation operators (i.e., resource-based mutation) in the evolutionary search that take into account the use of these resources in the generated test cases. Moreover, we propose a novel dependency handling to detect possible dependencies among the resources in the tested applications. The resource-based sampling and mutations are then enhanced by exploiting the information of these detected dependencies. To evaluate our approach, we implemented it as an extension to the EvoMaster tool, and conducted an empirical study with two selected baselines on 7 open-source and 12 synthetic RESTful web services. Results show that our novel resource-based approach with dependency handling obtains a significant improvement in performance over the baselines, e.g., up to + 130.7% relative improvement (growing from + 27.9% to + 64.3%) on line coverage.

Nowadays, RESTful web services are widely used for building enterprise applications. In this paper, we propose an enhanced search-based method for automated system test generation for RESTful web services. This method exploits domain knowledge on the handling of HTTP resources, and it is integrated in the Many Independent Objectives (MIO) search algorithm. MIO is an evolutionary algorithm specialized for system test case generation with the aim of maximizing code coverage and fault finding. Our approach builds on top of the MIO by implementing a set of effective templates to structure test actions, based on the semantics of HTTP methods, used to manipulate the web services' resources. We propose four novel sampling strategies for the test cases that can use one or more of these test actions. The strategies are further supported with a set of new, specialized mutation operators that take into account the use of these resources in the generated test cases. We implemented our approach as an extension to the EvoMaster tool, and evaluated it on seven open-source RESTful web services. The results of our empirical study show that our novel, resource-based sampling strategies obtain a significant improvement in performance over the baseline MIO (up to +42% coverage).

In the context of robustness testing, the boundary between the valid and invalid regions of the input space can be an interesting source of erroneous inputs. Knowing where a specific software under test (SUT) has a boundary is also essential for validation in relation to requirements. However, finding where a SUT actually implements the boundary is a non-trivial problem that has not gotten much attention. This paper proposes a method of finding the boundary between the valid and invalid regions of the input space, by developing pairs of test sets that describe that boundary in detail. The proposed method consists of two steps. First, test data generators, directed by a search algorithm to maximise distance to known, valid test cases, generate valid test cases that are closer to the boundary. Second, these valid test cases undergo mutations to try to push them over the boundary and into the invalid part of the input space. This results in a pair of test sets, one consisting of test cases on the valid side of the boundary and a matched set on the outer side, with only a small distance between the two sets. The method is evaluated on a number of examples from the standard library of a modern programming language. We propose a method of determining the boundary between valid and invalid regions of the input space, and apply it on a SUT that has a non-contiguous valid region of the input space. From the small distance between the developed pairs of test sets, and the fact that one test set contains valid test cases and the other invalid test cases, we conclude that the pair of test sets described the boundary between the valid and invalid regions of that input space. Differences of behaviour can be observed between different distances and different sets of mutation operators, but all show that the method is able to identify the boundary between the valid and invalid regions of the input space. This is an important step towards more automated robustness testing.

Search-based software testing (SBST) often uses objective-based approaches to solve testing problems. There are, however, situations where the validity and completeness of objectives cannot be ascertained, or where there is insufficient information to define objectives at all. Incomplete or incorrect objectives may steer the search away from interesting behavior of the software under test (SUT) and from potentially useful test cases. This papers investigates the degree to which exploration-based algorithms can be used to complement an objective-based tool we have previously developed and evaluated in industry. In particular, we would like to assess how exploration-based algorithms perform in situations where little information on the behavior space is available a priori. We have conducted an experiment comparing the performance of an exploration-based algorithm with an objective-based one on a problem with a high-dimensional behavior space. In addition, we evaluate to what extent that performance degrades in situations where computational resources are limited. Our experiment shows that exploration-based algorithms are useful in covering a larger area of the behavior space and result in a more diverse solution population. Typically, of the candidate solutions that exploration-based algorithms propose, more than 80% were not covered by their objective-based counterpart. This increased diversity is present in the resulting population even when computational resources are limited. We conclude that exploration-focused algorithms are a useful means of investigating high-dimensional spaces, even in situations where limited information and limited resources are available.

Search-based software testing uses meta-heuristic search techniques to automate or partially automate testing tasks, such as test case generation or test data generation. It uses a fitness function to encode the quality characteristics that are relevant, for a given problem, and guides the search to acceptable solutions in a potentially vast search space. From an industrial perspective, this opens up the possibility of generating and evaluating lots of test cases without raising costs to unacceptable levels. First, however, the applicability of search-based software engineering in an industrial setting must be evaluated. In practice, it is difficult to develop a priori a fitness function that covers all practical aspects of a problem. Interaction with human experts offers access to experience that is otherwise unavailable and allows the creation of a more informed and accurate fitness function. Moreover, our industrial partner has already expressed a view that the knowledge and experience of domain specialists are more important to the overall quality of the systems they develop than software engineering expertise. In this paper we describe our application of Interactive Search Based Software Testing (ISBST) in an industrial setting. We used SBST to search for test cases for an industrial software module and based, in part, on interaction with a human domain specialist. Our evaluation showed that such an approach is feasible, though it also identified potential difficulties relating to the interaction between the domain specialist and the system.

Even hardware-focused industries today develop products where software is both a large and important component. Engineers tasked with developing and integrating these products do not always have a software engineering background. To ensure quality, tools are needed that automate and support software testing while allowing these domain specialists to leverage their knowledge and experience. Search-based testing could be a key aspect in creating an automated tool for supporting testing activities. However, domain specific quality criteria and trade-offs make it difficult to develop a general fitness function a priori, so interaction between domain specialists and such a tool would be critical to its success. In this paper we present a system for interactive search-based software testing and investigate a way for domain specialists to guide the search by dynamically re-weighting quality goals. Our empirical investigation shows that objective re-weighting can help a human domain specialist interactively guide the search, without requiring specialized knowledge of the system and without sacrificing population diversity.