There have been rapid recent developments in automated software test design, repair and program improvement. Advances in artificial intelligence also have great potential impact to tackle software testing research problems. In this paper we highlight open research problems and challenges from an industrial perspective. This perspective draws on our experience at Meta Platforms, which has been actively involved in software testing research and development for approximately a decade. As we set out here, there are many exciting opportunities for software testing research to achieve the widest and deepest impact on software practice. With this overview of the research landscape from an industrial perspective, we aim to stimulate further interest in the deployment of software testing research. We hope to be able to collaborate with the scientific community on some of these research challenges.

This paper outlines 4 open challenges for Software Testing in general and Search Based Software Testing in particular, arising from our experience with the Sapienz System Deployment at Facebook. The challenges may also apply more generally, thereby representing opportunities for the research community to further benefit from the growing interest in automated test design in industry.

We describe the deployment of the Sapienz Search Based Software Engineering (SBSE) testing system. Sapienz has been deployed in production at Facebook since September 2017 to design test cases, localise and triage crashes to developers and to monitor their fixes. Since then, running in fully continuous integration within Facebook’s production development process, Sapienz has been testing Facebook’s Android app, which consists of millions of lines of code and is used daily by hundreds of millions of people around the globe. We continue to build on the Sapienz infrastructure, extending it to provide other software engineering services, applying it to other apps and platforms, and hope this will yield further industrial interest in and uptake of SBSE (and hybridisations of SBSE) as a result. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Summary This paper presents a case study of coverage‐based regression testing techniques on a real world industrial system with real regression faults. The study evaluates four common prioritization techniques, a test selection technique, a test suite minimization technique and a hybrid approach that combines selection and minimization. The study also examines the effects of using various coverage criteria on the effectiveness of the studied approaches. The results show that prioritization techniques that are based on additional coverage with finer grained coverage criteria perform significantly better in fault detection rates. The study also reveals that using modification information in prioritization techniques does not significantly enhance fault detection rates. The results show that test selection does not provide significant savings in execution cost (<2%), which might be attributed to the nature of the changes made to the system. Test suite minimization using finer grained coverage criteria could provide significant savings in execution cost (79.5%) while maintaining a fault detection capability level above 70%, thus representing a possible trade‐off. The hybrid technique did not provide a significant improvement over traditional minimization techniques. Copyright © 2015 John Wiley & Sons, Ltd.

This paper studies the whitebox coverage and fault detection achieved by Output Uniqueness, a newly proposed blackbox test criterion, using 6 web applications. We find that output uniqueness exhibits average correlation coefficients of 0.85, 0.83 and 0.97 with statement, branch and path coverage respectively. More interestingly, output uniqueness finds 92% of the real faults found by branch coverage (and a further 47% that remained undetected by such whitebox techniques). These results suggest that output uniqueness may provide a useful surrogate when whitebox techniques are inapplicable and an effective complement where they are.

Web services are increasingly adopted in various domains, from finance and e-government to social media. As they are built on top of the web technologies, they suffer also an unprecedented amount of attacks and exploitations like the Web. Among the attacks, those that target SQL injection vulnerabilities have consistently been top-ranked for the last years. Testing to detect such vulnerabilities before making web services public is crucial. We present in this paper an automated testing approach, namely μ4SQLi, and its underpinning set of mutation operators. μ4SQLi can produce effective inputs that lead to executable and harmful SQL statements. Executability is key as otherwise no injection vulnerability can be exploited. Our evaluation demonstrated that the approach is effective to detect SQL injection vulnerabilities and to produce inputs that bypass application firewalls, which is a common configuration in real world.

This paper presents an industrial case study of coverage-based prioritisation techniques on a real world system with real regression faults. The study evaluates four common and different test case prioritisation techniques and examines the effects of using various coverage criteria on the fault detection rates of the prioritised test suites. The results show that prioritisation techniques that are based on additional coverage with finer grained coverage criteria perform significantly better in fault detection rates. The study also reveals that using modification information does not significantly enhance fault detection rates.

This paper presents an automated, model based test validation and oracle approach for systems with complex input and output structures, such as Data Acquisition (DAQ) systems, which are common in many sectors including the satellite communications industry. We present a customised modelling methodology for such systems and a tool that automatically validates test inputs and, after test execution, applies an oracle that is based on mappings between the input and output. We also apply our proposed approach and tool to a complex industrial DAQ system and investigate the scalability and effectiveness of the approach in validating test cases, the DAQ system, or its specifications (captured as models). The results of the case study show that the approach is indeed scalable with respect to two dimensions: (1) model size and (2) test validation and oracle execution time. The size of the model for the DAQ system under study remains within practical bounds, and far below that of typical system models, as it includes a class diagram with 68 classes and 49 constraints. The developed test validation and oracles tool can handle satellite transmission files up to two GB within practical time constraints, taking, on a standard PC, less than three minutes for test validation and less than 50 minutes for applying the oracle. The approach was also effective in automatically applying the oracle successfully for the actual test suite of the DAQ system, accurately identifying all issues and violations that were expected, thus showing that an approach based on models can be sufficiently accurate.

The uniqueness (or otherwise) of test outputs ought to have a bearing on test effectiveness, yet it has not previously been studied. In this paper we introduce a novel test suite adequacy criterion based on output uniqueness. We propose 4 definitions of output uniqueness with varying degrees of strictness. We present a preliminary evaluation for web application testing that confirms that output uniqueness enhances fault-finding effectiveness. The approach outperforms random augmentation in fault finding ability by an overall average of 280% in 5 medium sized, real world web applications.

Automated web crawlers can be used to explore and exercise portions of a web application under test. However, the possibility to achieve full exploration of a web application through automated crawling is severely limited by the choice of the input values submitted with forms. Depending on the crawler's capabilities, a larger or smaller portion of web application will be automatically explored. In this paper, we introduce web crawl ability metrics to quantify properties of application pages and forms that affect crawl ability. Moreover, we show that our metrics can be used to identify the boundaries between those parts of the application that can be successfully crawled automatically and those parts that will require manual intervention or other crawl ability support. We have validated our crawl ability metrics on real web applications, for which low crawl ability was indeed associated with the existence of pages never exercised during automated crawling.

This paper introduces two test cases regeneration approaches for web applications, one uses standard Def-Use testing but for state variables, the other uses a novel value-aware dataflow approach. Our overall approach is to combine requests from a test suite to form client-side request sequences, based on dataflow analysis of server-side session variables and database tables. We implemented our approach as a tool SART (State Aware Regeneration Tool) and used it to evaluate our proposed approaches on 4 real world web applications. Our results show that for all 4 applications, both server-side coverage and fault detection were statistically significantly improved. Even on relatively high quality test suites our algorithms improve average coverage by 14.74% and fault detection by 9.19%.

This paper introduces three related algorithms and a tool, SWAT, for automated web application testing using Search Based Software Testing (SBST). The algorithms significantly enhance the efficiency and effectiveness of traditional search based techniques exploiting both static and dynamic analysis. The combined approach yields a 54% increase in branch coverage and a 30% reduction in test effort. Each improvement is separately evaluated in an empirical study on 6 real world web applications.

This paper introduces an approach to web application regression testing, based upon repair of user session data. The approach is entirely automated. It consists of a white box examination of the structure of the changed web application to detect changes and a set of techniques to map these detected changes onto repair actions. The paper reports the results of experiments that explore both the performance and effectiveness of the approach. The effectiveness experiment uses an implementation of the repair algorithm applied to the online bookstore application over a series of 10 releases.