In RESTful APIs, interactions with a database are a common and crucial aspect. When generating white-box tests, it is essential to consider the database’s state (i.e., the data contained in the database) to achieve higher code coverage and uncover more hidden faults. This article presents novel techniques to enhance search-based software test generation for RESTful APIs interacting with NoSQL databases. Specifically, we target the popular MongoDB database by dynamically analyzing its state—via automated code instrumentation—during test generation, with the goal of producing non-empty results for the queries executed by the generated tests. Additionally, to achieve better results, our novel approach also allows inserting automatically generated NoSQL data directly from test cases. This is particularly beneficial when testing read-only microservices, or when it is challenging or time-consuming to generate the correct sequence of HTTP calls to populate the NoSQL database. Our novel techniques are implemented as an extension of EvoMaster, the only open-source tool for white-box fuzzing RESTful APIs. Experiments conducted on six RESTful APIs demonstrated significant improvements in code coverage, with increases of up to 18% compared to existing white-box approaches. To better highlight the improvements of our novel techniques, comparisons are also carried out with four state-of-the-art black-box fuzzers.

Abstract Search-Based Software Testing (SBST) has seen several success stories in academia and industry. The effectiveness of a search algorithm at solving a software engineering problem strongly depends on how such algorithm can navigate the fitness landscape of the addressed problem. The fitness landscape depends on the used fitness function. Understanding the properties of a fitness landscape can help to provide insight on how a search algorithm behaves on it. Such insight can provide valuable information to researchers to being able to design novel, more effective search algorithms and fitness functions tailored for a specific problem. Due to its importance, few fitness landscape analyses have been carried out in the scientific literature of SBST. However, those have been focusing on the problem of unit test generation, e.g., with state-of-the-art tools such as EvoSuite. In this paper, we replicate one such existing study. However, in our work we focus on system test generation, with the state-of-the-art tool EvoMaster . Based on an empirical study involving the testing of 23 web services, this enables us to provide valuable insight into this important testing domain of practical industrial relevance. Our results indicate that fitness landscapes are largely dominated by neutral regions (e.g., plateaus), which make the search process challenging. We observe that the presence of information content in the landscape can improve search guidance, while boolean flags are a primary contributor to neutrality. These findings confirm prior results in unit testing but also reveal system-level differences, particularly in how branch types impact search effectiveness. These insights suggest the need for improved fitness functions, testability transformations, and search operators tailored to system-level testing.

In this article, we explore the impact of tool development and its evolution in Search-Based Software Engineering (SBSE) research. As a research tool evolves throughout the years, experiments with novel techniques might require reevaluation of previous studies, especially regarding parameter tuning. These reevaluations also give the opportunity to address the threats to external validity of these previous studies by employing a larger selection of artifacts. To conduct the replicated experiments in this study, the search-based fuzzer EvoMaster is chosen. This SBSE tool has been developed and extended throughout several years (since 2016) and tens of scientific studies. Among the chosen tool's parameters, 6 were carefully selected based on 5 previous studies that we replicate in this article with the latest version of EvoMaster. The replication is applied across an expanded set of artifacts compared to the original replicated studies. Our objective is to validate the robustness and validity of previous findings and to determine the need for parameter tuning in response to the tool's continuous development. Beyond replication, we explored parameter tuning by testing 729 different configurations to find a more performant parameter set, which is later validated through additional rounds of experiments. Additionally, we analyzed the impact of individual parameters on test generation performance using machine learning models, providing insights into their relative effects. Our findings indicate that, although most parameters maintain their efficacy, 2 of them require adjustment. Furthermore, the investigation into the effects of combining different parameter values reveals that carefully optimized configurations can outperform default settings. These findings highlight the importance of regularly reevaluating parameter settings to enhance tool performance in SBSE research.

Testing large and complex enterprise software systems can be a challenging task. This is especially the case when the functionality of the system depends on interactions with other external services over a network (e.g., external web services accessed through REST API calls). Although several techniques in the research literature have been shown to be effective at generating test cases in a good number of different software testing contexts, dealing with external services is still a major research challenge. In industry, a common approach is to mock external web services for testing purposes. However, generating and configuring mock web services can be a very time-consuming task, e.g., external services may not be under the control of the same developers of the tested application, making it challenging to identify the external services and simulate various possible responses. In this article, we present a novel search-based approach aimed at fully automated mocking of external web services as part of white-box, search-based fuzzing. We rely on code instrumentation to detect all interactions with external services, and how their response data is parsed. We then use such information to enhance a search-based approach for fuzzing. The tested application is automatically modified (by manipulating DNS lookups) to rather interact with instances of mock web servers. The search process not only generates inputs to the tested applications but also automatically configures responses in those mock web server instances, aiming at maximizing code coverage and fault-finding. An empirical study on four open source REST APIs from EMB, and one industrial API from an industry partner, shows the effectiveness of our novel techniques (i.e., in terms of line coverage and fault detection).

Testing Autonomous Driving Systems (ADS) is critical to ensure their safety and reliability in dynamic and unpredictable real-world driving environments.In the literature, many scenario-based ADS testing solutions have been proposed to generate safety-critical driving scenarios.Along a similar research line, in this paper, we present a tool, named LiveTCM, which has a web-based model editor for specifying and executing Test Case Specifications (TCS).LiveTCM also has an extensible engine for enabling generation of TCS via real-time communication with the ADS (i.e., the system under test) situated in a simulated ADS driving environment.Videos illustrating the capabilities of LiveTCM can be found at: https://github.com/WSE-Lab/LiveTCM.

Due to their widespread use in industry, several techniques have been proposed in the literature to fuzz REST APIs. Existing fuzzers for REST APIs have been focusing on detecting crashes (e.g., 500 HTTP server error status code). However, security vulnerabilities can have major drastic consequences on existing cloud infrastructures.In this paper, we propose a series of novel automated oracles aimed at detecting violations of access policies in REST APIs. These novel automated oracles can be integrated into existing fuzzers, in which, once the fuzzing session is completed, a “security testing” phase is executed to verify these oracles.Our novel techniques are integrated as an extension of EVO-MASTER, a state-of-the-art fuzzer for REST APIs. Experiments are carried out on a series of artificial examples and 13 real-world REST APIs. Results show that our novel oracles and their automated integration in a fuzzing process can lead to detect security issues in some of these APIs.

In this paper, we present the latest version 3.0.0 of EvoMaster, an open-source search-based fuzzer aimed at Web APIs. We discuss and present all its recent improvements, including advanced white-box heuristics, advanced search algorithms, support for databases and external services, as well as dealing with GraphQL and RPC APIs besides the original use case for REST APIs. The tool's installers have been downloaded more than 3000 times. EvoMaster is in daily use for fuzzing millions of lines of code in hundreds of APIs in large Fortune 500 companies, such as for example the e-commerce Meituan.

With several microservice architectures comprising thousands of web services in total, used to serve 630 million customers, companies like Meituan face several challenges in the verification and validation of their software. The use of automated techniques, especially advanced AI-based ones, could bring significant benefits here. EvoMaster is an open-source test case generation tool for web services, that exploits the latest advances in the field of Search-Based Software Testing research. This paper reports on our experience of integrating the EvoMaster tool in the testing processes at Meituan over almost 2 years (i.e., between October 2021 and July 2023). Two user studies were carried out in 2021 (with two industrial APIs) and in 2023 (with three industrial APIs) to evaluate two versions of EvoMaster (i.e., v1.3.0 and v1.6.1), respectively, in tackling the test generation for industrial web services which are parts of a large e-commerce microservice system. The two user studies involve in total 321,131 lines of code from these five APIs and 27 industrial participants at Meituan. Questionnaires and interviews were carried out in both user studies with the engineers and managers at Meituan. The two user studies demonstrate clear advantages of EvoMaster (in terms of code coverage and fault detection) and the urgent need to have such a fuzzer in industrial microservices testing. Given its clear advantages, EvoMaster now has been integrated into the industrial testing pipelines at Meituan. To study how these results could generalize, a follow up user study was done in 2024 (with EvoMaster v2.0.0) with five engineers in the five different companies. Our results show that, besides their clear usefulness, there are still many critical challenges that the research community needs to investigate to improve performance further.

Microservices is now becoming a promising architecture to build large-scale web services in industry. Due to the high complexity of enterprise microservices, industry has an urgent need to have a solution to enable automated testing of such systems. EvoMaster is an open-source fuzzer, equipped with the state-of-the-art techniques for supporting automated system-level testing of Web APIs. It has been assessed as the most performant tool in two recent empirical studies in terms of line coverage and fault detection. In this paper, we carried out an empirical experiment to investigate how to better apply the state-of-the-art academic prototype (i.e., EvoMaster) in industrial context. We extended the tool to handle seeding of existing industrial tests, and mocking of external services with their data handled as part of the input fuzzing. We studied two configurations of EvoMaster, using two time budgets, on 40 enterprise RPC-based APIs (involving 5.6 million lines of code for their core business logic) at Meituan. Results show that, compared to existing practice of manual system-level testing and tests produced by record and replay of online traffic, EvoMaster demonstrates clear additional benefits. EvoMaster with the best configuration is capable of covering up to 32.4% line coverage, covering more than 10% line coverage on 36 out of 40 (90%) case studies, and identifying on average 3520 potential faults in these 40 APIs. In addition, we also identified and discussed important challenges in fuzzing enterprise microservices that must be addressed in the future.

Due to its importance and widespread use in industry, automated testing of REST APIs has attracted major interest from the research community in the last few years. However, most of the work in the literature has been focused on black-box fuzzing. Although existing fuzzers have been used to automatically find many faults in existing APIs, there are still several open research challenges that hinder the achievement of better results (e.g., in terms of code coverage and fault finding). For example, under-specified schemas are a major issue for black-box fuzzers. Currently, EvoMaster is the only existing tool that supports white-box fuzzing of REST APIs. In this paper, we provide a series of novel white-box heuristics, including for example how to deal with under-specified constrains in API schemas, as well as under-specified schemas in SQL databases. Our novel techniques are implemented as an extension to our open-source, search-based fuzzer EvoMaster . An empirical study on 14 APIs from the EMB corpus, plus one industrial API, shows clear improvements of the results in some of these APIs.

In industry, RESTful APIs are widely used to build modern Cloud Applications. Testing them is challenging, because not only do they rely on network communications, but also they deal with external services like databases. Therefore, there has been a large amount of research sprout in recent years on how to automatically verify this kind of web services. In this article, we present a comprehensive review of the current state-of-the-art in testing RESTful APIs based on the analysis of 92 scientific articles. These articles were gathered by utilizing search queries formulated around the concept of RESTful API testing on seven popular databases. We eliminated irrelevant articles based on our predefined criteria and conducted a snowballing phase to minimize the possibility of missing any relevant paper. This survey categorizes and summarizes the existing scientific work on testing RESTful APIs and discusses the current challenges in the verification of RESTful APIs. This survey clearly shows an increasing interest among researchers in this field, from 2017 onward. However, there are still a lot of open research challenges to overcome.

The Graph Query Language (GraphQL) is a powerful language for application programming interface (API) manipulation in web services. It has been recently introduced as an alternative solution for addressing the limitations of RESTful APIs. This article introduces an automated solution for GraphQL API testing. We present a full framework for automated API testing, from the schema extraction to test case generation. In addition, we consider two kinds of testing: white-box and black-box testing. The white-box testing is performed when the source code of the GraphQL API is available. Our approach is based on evolutionary search. Test cases are evolved to intelligently explore the solution space while maximizing code coverage and fault-finding criteria. The black-box testing does not require access to the source code of the GraphQL API. It is therefore of more general applicability, albeit it has worse performance. In this context, we use a random search to generate GraphQL data. The proposed framework is implemented and integrated into the open source EvoMaster tool. With enabled white-box heuristics (i.e., white-box mode), experiments on 7 open source GraphQL APIs and three search algorithms show statistically significant improvement of the evolutionary approach compared to the baseline random search. In addition, experiments on 31 online GraphQL APIs reveal the ability of the black-box mode to detect real faults.

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.

Abstract C# is one of the most widely used programming languages. However, to the best of our knowledge, there has been no work in the literature aimed at enabling search-based software testing techniques for applications running on the .NET platform, like the ones written in C#. In this paper, we propose a search-based approach and an open source tool to enable white-box testing for C# applications. The approach is integrated with a .NET bytecode instrumentation, in order to collect code coverage at runtime during the search. In addition, by taking advantage of Branch Distance, we define heuristics to better guide the search, e.g., how heuristically close it is to cover a branch in the source code. To empirically evaluate our technique, we integrated our tool into the EvoMaster test generation tool and conducted experiments on three .NET RESTful APIs as case studies. Results show that our technique significantly outperforms gray-box testing tools in terms of code coverage.

RESTful APIs are a type of web service that are widely used in industry. In the past few years, a lot of effort in the research community has been spent in designing novel techniques to automatically fuzz those APIs to find faults in them. Many real faults were automatically found in a large variety of RESTful APIs. However, usually the analyzed fuzzers treat the APIs as black-box, and no analysis of what is actually covered in these systems is done. Therefore, although these fuzzers are clearly useful for practitioners, we do not know their current limitations and actual effectiveness. Solving this is a necessary step to be able to design better, more efficient, and effective techniques. To address this issue, in this article we compare seven state-of-the-art fuzzers on 18 open source—1 industrial and 1 artificial—RESTful APIs. We then analyze the source code for which parts of these APIs the fuzzers fail to generate tests. This analysis points to clear limitations of these current fuzzers, listing concrete follow-up challenges for the research community.

Remote Procedure Call (RPC) is a communication protocol to support client-server interactions among services over a network. RPC is widely applied in industry for building large-scale distributed systems, such as Microservices. Modern RPC frameworks include, for example, Thrift, gRPC, SOFARPC, and Dubbo. Testing such systems using RPC communications is very challenging, due to the complexity of distributed systems and various RPC frameworks the system could employ. To the best of our knowledge, there does not exist any tool or solution that could enable automated testing of modern RPC-based services. To fill this gap, in this article we propose the first approach in the literature, together with an open source tool, for fuzzing modern RPC-based APIs. The approach is in the context of white-box testing with search-based techniques. To tackle schema extraction of various RPC frameworks, we formulate a RPC schema specification along with a parser that allows the extraction from source code of any JVM RPC-based APIs. Then, with the extracted schema we employ a search to produce tests by maximizing white-box heuristics and newly defined heuristics specific to the RPC domain. We built our approach as an extension to an open source fuzzer (i.e., EvoMaster ), and the approach has been integrated into a real industrial pipeline that could be applied to a real industrial development process for fuzzing RPC-based APIs. To assess our novel approach, we conducted an empirical study with two artificial and four industrial web services selected by our industrial partner. In addition, to further demonstrate its effectiveness and application in industrial settings, we report results of employing our tool for fuzzing another 50 industrial APIs autonomously conducted by our industrial partner in their testing processes. Results show that our novel approach is capable of enabling automated test case generation for industrial RPC-based APIs (i.e., 2 artificial and 54 industrial). We also compared with a simple gray-box technique and existing manually written tests. Our white-box solution achieves significant improvements on code coverage. Regarding fault detection, by conducting a careful review with our industrial partner of the tests generated by our novel approach in the selected four industrial APIs, a total of 41 real faults were identified, which have now been fixed. Another 8,377 detected faults are currently under investigation.

JavaScript is one of the most popular programming languages. However, its dynamic nature poses several challenges to automated testing techniques. In this paper, we propose an approach and open-source tool support to enable white-box testing of JavaScript applications using Search-Based Software Testing (SBST) techniques. We provide an automated approach to collect search-based heuristics like the common Branch Distance and to enable Testability Transformations . To empirically evaluate our results, we integrated our technique into the EvoMaster test generation tool, and carried out analyses on the automated system testing of RESTful and GraphQL APIs. Experiments on eight Web APIs running on NodeJS show that our technique leads to significantly better results than existing black-box and grey-box testing tools, in terms of code coverage and fault detection.

Autonomous vehicles must operate safely in their dynamic and continuously-changing environment. However, the operating environment of an autonomous vehicle is complicated and full of various types of uncertainties. Additionally, the operating environment has many configurations, including static and dynamic obstacles with which an autonomous vehicle must avoid collisions. Though various approaches targeting environment configuration for autonomous vehicles have shown promising results, their effectiveness in dealing with a continuous-changing environment is limited. Thus, it is essential to learn realistic environment configurations of continuously-changing environment, under which an autonomous vehicle should be tested regarding its ability to avoid collisions. Featured with agents dynamically interacting with the environment, Reinforcement Learning (RL) has shown great potential in dealing with complicated problems requiring adapting to the environment. To this end, we present an RL-based environment configuration learning approach, i.e.,DeepCollision, which intelligently learns environment configurations that lead an autonomous vehicle to crash. DeepCollision employs Deep Q-Learning as the RL solution, and selectscollision probabilityas the safety measure, to construct the reward function. We trained four DeepCollision models and conducted an experiment to compare them with two baselines, i.e., random and greedy. Results show that DeepCollision demonstrated significantly better effectiveness in generating collisions compared with the baselines. We also provide recommendations on configuring DeepCollision with the most suitable time interval based on different road structures.

JavaScript is one of the most popular programming languages. However, its dynamic nature poses several challenges to automated testing techniques. In this paper, we propose an approach and open-source tool support to enable white-box testing of JavaScript applications using Search-Based Software Testing techniques. We provide an automated approach to collect search-based heuristics like the common Branch Distance. To empirically evaluate our results, we integrated our technique into the EvoMASTER test generation tool, and carried out analyses on the automated system testing of RESTful APIs. Experiments on 5 NodeJS APIs show that our technique leads to significantly better results than existing black-box and grey-box testing tools in terms of code coverage and fault detection.

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.

REST web services are widely popular in industry, and search techniques have been successfully used to automatically generate system-level test cases for those systems. In this article, we propose a novel mutation operator which is designed specifically for test generation at system-level, with a particular focus on REST APIs. In REST API testing, and often in system testing in general, an individual can have a long and complex chromosome. Furthermore, there are two specific issues: (1) fitness evaluation in system testing is highly costly compared with the number of objectives (e.g., testing targets) to optimize for; and (2) a large part of the genotype might have no impact on the phenotype of the individuals (e.g., input data that has no impact on the execution flow in the tested program). Due to these issues, it might be not suitable to apply a typical low mutation rate like 1/ n (where n is the number of genes in an individual), which would lead to mutating only one gene on average. Therefore, in this article, we propose an adaptive weight-based hypermutation, which is aware of the different characteristics of the mutated genes. We developed adaptive strategies that enable the selection and mutation of genes adaptively based on their fitness impact and mutation history throughout the search. To assess our novel proposed mutation operator, we implemented it in the EvoMaster tool, integrated in the MIO algorithm, and further conducted an empirical study with three artificial REST APIs and four real-world REST APIs. Results show that our novel mutation operator demonstrates noticeable improvements over the default MIO. It provides a significant improvement in performance for six out of the seven case studies, where the relative improvement is up to +12.09% for target coverage, +12.69% for line coverage, and +32.51% for branch coverage.

With the aim to reduce car accidents, autonomous driving attracted a lot of attentions these years. However, recently reported crashes indicate that this goal is far from being achieved. Hence, cost-effective testing of autonomous driving systems (ADSs) has become a prominent research topic. The classical model-based testing (MBT), i.e., generating test cases from test models followed by executing the test cases, is ineffective for testing ADSs, mainly because of the constant exposure to ever-changing operating environments, and uncertain internal behaviors due to employed AI techniques. Thus, MBT must be adaptive to guide test case generation based on test execution results in a step-wise manner. To this end, we propose a natural language and model-based approach, named LiveTCM, to automatically execute and generate test case specifications (TCSs) by interacting with an ADS under test and its environment. LiveTCM is evaluated with an open-source ADS and two test generation strategies: Deep Q-Network (DQN)-based and Random. Results show that LiveTCM with DQN can generate TCSs with 56 steps on average in 60 seconds, leading to 6.4 test oracle violations and covering 14 APIs per TCS on average.

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.

Requirements review is an effective technique to ensure the quality of requirements in practice, especially in safety-critical domains (e.g., avionics systems, automotive systems). In such contexts, a typical requirements review process often prioritizes requirements, due to limited time and monetary budget, by, for instance, prioritizing requirements with higher implementation cost earlier in the review process. However, such a requirement implementation cost is typically estimated by stakeholders who often lack knowledge about (future) requirements implementation scenarios, which leads to uncertainty in cost overrun. In this article, we explicitly consider such uncertainty (quantified as cost overrun probability) when prioritizing requirements based on the assumption that a requirement with higher importance, a higher number of dependencies to other requirements, and higher implementation cost will be reviewed with the higher priority. Motivated by this, we formulate four objectives for uncertainty-wise requirements prioritization: maximizing the importance of requirements, requirements dependencies, the implementation cost of requirements, and cost overrun probability. These four objectives are integrated as part of our search-based uncertainty-wise requirements prioritization approach with tool support, named as URP. We evaluated six Multi-Objective Search Algorithms (MOSAs) (i.e., NSGA-II, NSGA-III, MOCell, SPEA2, IBEA, and PAES ) together with Random Search ( RS ) using three real-world datasets (i.e., the RALIC, Word, and ReleasePlanner datasets) and 19 synthetic optimization problems. Results show that all the selected MOSAs can solve the requirements prioritization problem with significantly better performance than RS . Among them, IBEA was over 40% better than RS in terms of permutation effectiveness for the first 10% of prioritized requirements in the prioritization sequence of all three datasets. In addition, IBEA achieved the best performance in terms of the convergence of solutions, and NSGA-III performed the best when considering both the convergence and diversity of nondominated solutions.

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).

It is important that a Cyber-Physical System (CPS) with uncertainty in its behavior caused by its unpredictable operating environment, to ensure its reliable operation. One method to ensure that the CPS will handle such uncertainty during its operation is by testing the CPS with model-based testing (MBT) techniques. However, existing MBT techniques do not explicitly capture uncertainty in test ready models, i.e., capturing the uncertain expected behavior of a CPS in the presence of environment uncertainty. To fill this gap, we present an Uncertainty-Wise test-modeling framework, named as UncerTum , to create test ready models to support MBT of CPSs facing uncertainty. UncerTum relies on the definition of a UML profile [the UML Uncertainty Profile ( UUP )] and a set of UML Model Libraries extending the UML profile for Modeling and Analysis of Real-Time and Embedded Systems (MARTE). UncerTum also benefits from the UML Testing Profile V.2 to support standard-based MBT. UncerTum was evaluated with two industrial CPS case studies, one real-world case study, and one open-source CPS case study from the following four perspectives: (1) Completeness and Coverage of the profiles and Model Libraries in terms of concepts defined in their underlying uncertainty conceptual model for CPSs, i.e., U-Model and MARTE, (2) Effort required to model uncertainty with UncerTum , and (3) Correctness of the developed test ready models, which was assessed via model execution. Based on the evaluation, we can conclude that we were successful in modeling all the uncertainties identified in the four case studies, which gives us an indication that UncerTum is sufficiently complete. In terms of modeling effort, we concluded that on average UncerTum requires 18.5% more time to apply stereotypes from UUP on test ready models.

To ensure the quality of requirements, a common practice, especially in critical domains, is to review requirements within a limited time and monetary budgets. A requirement with higher importance, larger number of dependencies with other requirements, and higher implementation cost should be reviewed with the highest priority. However, requirements are inherently uncertain in terms of their impact on the requirements implementation cost. Such cost is typically estimated by stakeholders as an interval, though an exact value is often used in the literature for requirements optimization (e.g., prioritization). Such a practice, therefore, ignores uncertainty inherent in the estimation of requirements implementation cost. This paper explicitly takes into account such uncertainty for requirement prioritization and formulates four objectives for uncertainty-wise requirements prioritization with the aim of maximizing 1) the importance of requirements, 2) requirements dependencies, 3) the implementation cost of requirements, and 4) cost over-run probability. We evaluated the multi-objective search algorithm NSGA-II together with Random Search (RS) using the RALIC dataset and 19 artificial problems. Results show that NSGA-II can solve the requirements prioritization problem with a significantly better performance than RS. Moreover, NSGA-II can prioritize requirements with higher priority earlier in the prioritization sequence. For example, in the case of the RALIC dataset, the first 10% of prioritized requirements in the prioritization sequence are on average 50% better than RS in terms of prioritization effectiveness.

Context Cyber-Physical Systems (CPSs), when deployed for operation, are inherently prone to uncertainty. Considering their applications in critical domains (e.g., healthcare), it is important that such CPSs are tested sufficiently, with the explicit consideration of uncertainty. Model-based testing (MBT) involves creating test ready models capturing the expected behavior of a CPS and its operating environment. These test ready models are then used for generating executable test cases. It is, therefore, necessary to develop methods that can continuously evolve, based on real operational data collected during the operation of CPSs, test ready models and uncertainty captured in them, all together termed as Belief Test Ready Models (BMs) Objective Our objective is to propose a model evolution framework that can interactively improve the quality of BMs, based on operational data. Such BMs are developed by one or more test modelers ( belief agents ) with their assumptions about the expected behavior of a CPS, its expected physical environment, and potential future deployments. Thus, these models explicitly contain subjective uncertainty of the test modelers. Method We propose a framework (named as UncerTolve ) for interactively evolving BMs (specified with extended UML notations) of CPSs with subjective uncertainty developed by test modelers. The key inputs of UncerTolve include initial BMs of CPSs with known subjective uncertainty and real data collected from the operation of CPSs. UncerTolve has three key features: 1) Validating the syntactic correctness and conformance of BMs against real operational data via model execution, 2) Evolving objective uncertainty measurements of BMs via model execution, and 3) Evolving state invariants (modeling test oracles) and guards of transitions (modeling constraints for test data generation) of BMs with a machine learning technique. Results As a proof-of-concept, we evaluated UncerTolve with one industrial CPS case study, i.e., GeoSports from the healthcare domain. Using UncerTolve , we managed to evolve 51% of belief elements, 18% of states, and 21% of transitions as compared to the initial BM developed in an industrial setting. Conclusion UncerTolve can successfully evolve model elements of the initial BM, in addition to objective uncertainty measurements using real operational data. The evolved model can be used to generate additional test cases covering evolved model elements and objective uncertainty. These additional test cases can be used to test the current and future deployments of a CPS to ensure that it will handle uncertainty gracefully during its operations.

Based on our experience of collaborating with industry, we observed that test case generation usually relies on test case specifications (TCSs), commonly written in natural language, specifying test cases of a System Under Test at a high level of abstraction. In practice, TCSs are commonly used by test engineers as reference documents to perform these activities: 1) Manually executing test cases in TCSs; 2) Manually coding test cases in a test scripting language for automated test case execution. In the latter case, the gap between TCSs and executable test cases has to be filled by test engineers, requiring a significant amount of coding effort and domain knowledge. Motivated by the above observations from the industry, we first propose, in this paper, a TCS language, named as Restricted Test Case Modeling (RTCM), which is based on natural language and composed of an easy-to-use template, a set of restriction rules and keywords. Second, we propose a test case generation tool (aToucan4Test), which takes TCSs in RTCM as input and generates either manual test cases or automatically executable test cases, based on various coverage criteria defined on RTCM. To assess the applicability of RTCM, we manually modeled two industrial case studies and examined 30 automatically generated TCSs. To evaluate aToucan4Test, we modeled three subsystems of a Video Conferencing System developed by Cisco Systems, Norway and automatically generated executable test cases. These test cases were successfully executed on two commercial software versions. In the paper, we also discuss our experience of applying RTCM and aToucan4Test in an industrial context and compare our approach with other model-based testing methodologies.