ABSTRACT Code review is a well‐established practice to ensure code quality, identify potential bugs, promote knowledge sharing and maintain coding standards within a team or organization. This study aims to investigate the specific practices, challenges and information needs encountered when reviewing GUI‐based test artefacts for web applications, which remain poorly understood. We conducted a qualitative interview study with 14 software testing professionals from six different companies to explore the distinct aspects of reviewing GUI‐based test artefacts. We identified four practices, six challenges and four information needs related to reviewing GUI‐based test artefacts. The foremost challenge is the validation of GUI‐based tests under review. Furthermore, challenges concerning levels of abstraction and test robustness were not addressed in related studies. Additionally, participants proposed six potential improvements for tools and practices to better support the code review process. Notably, the absence of standardized practices and the need to run tests locally were common themes across participants. The code review process for GUI‐based test artefacts differs from that of production code, highlighting the need for practices and tools tailored specifically to the unique demands of GUI‐based testing.

Generative Artificial Intelligence (GenAI) is increasinglyintegrated into software products to enable new features and user capabilities, from early exploration to operational deployment. GenAI adoption as a component within a software system introduces quality risks because GenAI outputs are probabilistic, prompt-sensitive, and may drift after release. Organizations, therefore, need to decide what to evaluate, when to evaluate, and who owns quality evaluation activities across software design, development, and operations. ISO/IEC 25059 standard distinguishes between software product quality (e.g., usability) and quality-in-use (e.g., satisfaction) for AI-enabled software, yet it provides limited operational guidance for these evaluation activities. We therefore investigate how industrial software teams adopt and use GenAI models in the software systems they build and operate, and how they evaluate system qualities when deciding to adopt GenAI during development and after deployment. We do not benchmark the underlying GenAI model itself. In this study, we conducted 19 semi-structured interviews in two software development companies. We triangulated the interviews with archival data (15 internal documents and 184 internal wiki/web pages) to capture GenAI adoption steps, quality concerns, evaluation practices, and role responsibilities. Our findings describe a three-phase adoption process – Ideation, Development, and Operation – highlighting where quality evaluations occur, which criteria are used, and how evaluation responsibilities are distributed. Based on observed practices and using ISO/IEC 25059 as an organizing lens, we synthesize a process-oriented quality evaluation framework. This framework maps metrics to explicit gatekeeping, validation, and monitoring checkpoints, bridging abstract ISO quality characteristics with engineering workflows. We applied the framework in a GenAI-enabled software product (SE4AI) use case and reported how it supported structured evaluation activities. We also observed that quality evaluations span legal, security, development, QA, and operations, but ownership is fragmented across phases. We therefore propose a GenAI Quality Lead responsibility (often assignable to an existing senior role) to coordinate criteria, evidence, and traceability across quality evaluation activities. The results contribute to Software Engineering for AI (SE4AI) by clarifying how teams can measure qualities when building software that adopts and uses GenAI.

Automated testing, particularly for GUI-based systems, remains a costly and labor-intensive process and prone to errors. Despite advancements in automation, manual testing still dominates in industrial practice, resulting in delays, higher costs, and increased error rates. Large Language Models (LLMs) have shown great potential to automate tasks traditionally requiring human intervention, leveraging their cognitive-like abilities for test generation and evaluation. In this study, we present PathFinder, a Multi-Agent LLM (MALLM) framework that incorporates four agents responsible for (a) perception and summarization, (b) decision-making, (c) input handling and extraction, and (d) validation, which work collaboratively to automate exploratory web-based GUI testing. The goal of this study is to assess how different LLMs, applied to different agents, affect the efficacy of automated exploratory GUI testing. We evaluate PathFinder with three models, Mistral-Nemo, Gemma2, and Llama3.1, on four e-commerce websites. Thus, 27 permutations of the LLMs, across three agents (excluding the validation agent), to test the hypothesis that a solution with multiple agents, each using different LLMs, is more efficacious (efficient and effective) than a multi-agent solution where all agents use the same LLM. The results indicate that the choice of LLM constellation (combination of LLMs) significantly impacts efficacy, suggesting that a single LLM across agents may yield the best balance of efficacy (measured by F1-score). Hypothesis to explain this result include, but are not limited to: improved decision-making consistency and reduced task coordination discrepancies. The contributions of this study are an architecture for MALLM-based GUI testing, empirical results on its performance, and novel insights into how LLM selection impacts the efficacy of automated testing.

Context: GUI-based tests for web applications are frequently broken by fragility, i.e. regression tests fail due to changing properties of the web elements. The most influential factor for fragility are the locators used in the scripts, i.e. the means of identifying the elements of the GUI. Objective: We extend a state-of-the-art Multi-Locator solution that considers 14 locators from the DOM model of a web application, and identifies overlapping nodes in the DOM tree (VON-Similo). We augment the approach with standard Machine Learning and Learning to Rank (LTR) approaches to aid the location of web elements. Method: We document an experiment with a ground truth of 1163 web element pairs, taken from different releases of 40 web applications, to compare the robustness of the algorithms to locator weight change, and the performance of LTR approaches in terms of MeanRank and PctAtN. Results: Using LTR algorithms, we obtain a maximum probability of finding the correct target at the first position of 88.4% (lowest 82.57%), and among the first three positions of 94.79% (lowest 91.86%). The best mean rank of the correct candidate is 1.57. Conclusion: The similarity-based approach proved to be highly dependable in the context of web application testing, where a low percentage of matching errors can still be accepted.

Generative AI (GenAI) is increasingly adopted in software development for tasks such as document generation, data analysis, and code generation. However, evaluating the quality of GenAI applications becomes challenging, as traditional quality measurements may not be fully applicable. In this study, we explore how practitioners evaluate the quality of GenAI applications and investigate quality evaluation techniques. We conducted a multi-case study in three industrial projects from software development companies. We examined four GenAI application domains: document generation, data analysis and insight generation, customer service, and code generation. Data were collected through three workshops and 23 semi-structured interviews with industrial practitioners. We identified fourteen GenAI use cases and 28 metrics currently used to evaluate the quality of GenAI applications’ outputs. We synthesized the identified metrics’ usage patterns and challenges based on the collected data. This study presents practical insights into using metrics to measure GenAI-based system qualities in real industrial settings. Our findings indicate that practitioners use custom-built and context-specific metrics; combining these with academic metrics can strengthen GenAI system quality evaluation.

Graphical User Interface (GUI) based testing is a commonly used practice in industry. Although valuable and, in many cases, necessary, it is associated with challenges such as high cost and requirements on both technical and domain expertise. Augmented testing, a novel approach to GUI test automation, aims to mitigate these challenges by allowing users to record and render test cases and test data directly on the GUI of the system under test (SUT). In this context, Scout is an augmented testing tool that captures system states and transitions during manual interaction with the SUT, storing them in a test model that is visually represented in the form of state trees and reports. While this representation provides basic overview of a test suite, e.g. about its size and number of scenarios, it is limited in terms of analysis depth, interpretability, and reproducibility. In particular, without human state labeling, it is challenging to produce meaningful and easily understandable test reports. To address this limitation, we present a novel solution and a demonstrator, integrated into Scout, which leverages large language models (LLMs) to enrich the model-based test case representation by automatically labeling and describing states and describing transitions. We conducted two experiments to evaluate the impact of the solution. First, we compared LLM-enhanced reports with expert-generated reports using embedding distance evaluation metrics. Second, we assessed the usability and perceived value of the enhanced reports through an industrial survey. The results of the study indicate that the plugin can improve readability, actionability, and interpretability of test reports. This work contributes to the automation of GUI testing by reducing the need for manual intervention, e.g. labeling, and technical expertise, e.g. to understand test case models. Although the solution is studied in the context of augmented testing, we argue for the solution's generalizability to related test automation techniques. In addition, we argue that this approach enables actionable insights and lays the groundwork for further research into autonomous testing based on Generative AI.

Context Generative Artificial Intelligence (GenAI) and the use of Large Language Models (LLMs) have revolutionized tasks that previously required significant human effort, which has attracted considerable interest from industry stakeholders. This growing interest has accelerated the integration of AI models into various industrial applications. However, the model integration introduces challenges to product quality, as conventional quality measuring methods may fail to assess GenAI systems. Consequently, evaluation techniques for GenAI systems need to be adapted and refined. Examining the current state and applicability of evaluation techniques for the GenAI system outputs is essential. Objective This study aims to explore the current metrics, methods, and processes for assessing the outputs of GenAI systems and the potential of risky outputs. Method We performed a snowballing literature review to identify metrics, evaluation methods, and evaluation processes from 43 selected papers. Results We identified 28 metrics and mapped these metrics to four quality characteristics defined by the ISO/IEC 25023 standard for software systems. Additionally, we discovered three types of evaluation methods to measure the quality of system outputs and a three-step process to assess faulty system outputs. Based on these insights, we suggested a five-step framework for measuring system quality while utilizing GenAI models. Conclusion Our findings present a mapping that visualizes candidate metrics to be selected for measuring quality characteristics of GenAI systems, accompanied by step-by-step processes to assist practitioners in conducting quality assessments.

Visualizing CI's role in software quality attribute evaluation.

Context : Gamification appears to improve enjoyment and quality of execution of software engineering activities, including software testing. Though commonly employed in industry, manual exploratory testing of web application GUIs was proven to be mundane and expensive. Gamification applied to that kind of testing activity has the potential to overcome its limitations, though no empirical research has explored this area yet.Goal: Collect preliminary insights on how gamification, when performed by novice testers, affects the effectiveness, efficiency, test case realism, and user experience in exploratory testing of web applications.Method: Common gamification features augment an existing exploratory testing tool: Final Score with Leaderboard, Injected Bugs, Progress Bar, and Exploration Highlights. The original tool and the gamified version are then compared in an experiment involving 144 participants. User experience is elicited using the Technology Acceptance Model (TAM) questionnaire instrument.Results: Statistical analysis identified several significant differences for metrics that represent the effectiveness and efficiency of tests showing an improvement in coverage when they were developed with gamification. Additionally, user experience is improved with gamification.Conclusions: Gamification of exploratory testing has a tangible effect on how testers create test cases for web applications. While the results are mixed, the effects are most beneficial and interesting and warrant more research in the future. Further research shall be aimed at confirming the presented results in the context of state-of-the-art testing tools and real-world development environments.

Background:Recent years have seen a surge in cyber-attacks, which can be prevented or mitigated using software security activities. OWASP SAMM is a maturity model providing a versatile way for companies to assess their security posture and plan for improvements.Objective:We perform an initial SAMM assessment in collaboration with a company in the financial domain. Our objective is to assess a holistic inventory of the company security-related activities, focusing on how different roles perform the assessment and how they perceive the instrument used in the process.Methodology:We perform a case study to collect data using SAMM in a lightweight and novel manner through assessment using an online survey with 17 participants and a focus group with seven participants.Results:We show that different roles perceive maturity differently and that the two assessments deviate only for specific practices making the lightweight approach a viable and efficient solution in industrial practice. Our results indicate that the questions included in the SAMM assessment tool are answered easily and confidently across most roles.Discussion:Our results suggest that companies can productively use a lightweight SAMM assessment. We provide nine lessons learned for guiding industrial practitioners in the evaluation of their current security posture as well as for academics wanting to utilize SAMM as a research tool in industrial settings.Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.

Large Language Models (LLMs) offer promising capabilities for information retrieval and processing. However, the LLM deployment for querying proprietary enterprise data poses unique challenges, particularly for companies with strict data security policies. This study shares our experience in setting up a secure LLM environment within a FinTech company and utilizing it for enterprise information retrieval while adhering to data privacy protocols. We conducted three workshops and 30 interviews with industrial engineers to gather data and requirements. The interviews further enriched the insights collected from the workshops. We report the steps to deploy an LLM solution in an industrial sandboxed environment and lessons learned from the experience. These lessons contain LLM configuration (e.g., chunk size and top k settings), local document ingestion, and evaluating LLM outputs. Our lessons learned serve as a practical guide for practitioners seeking to use private data with LLMs to achieve better usability, improve user experiences, or explore new business opportunities.

Web-based test automation heavily relies on accurately finding web elements. Traditional methods compare attributes but don't grasp the context and meaning of elements and words. The emergence of Large Language Models (LLMs) like GPT-4, which can show human-like reasoning abilities on some tasks, offers new opportunities for software engineering and web element localization. This paper introduces and evaluates VON Similo LLM, an enhanced web element localization approach. Using an LLM, it selects the most likely web element from the top-ranked ones identified by the existing VON Similo method, ideally aiming to get closer to human-like selection accuracy. An experimental study was conducted using 804 web element pairs from 48 real-world web applications. We measured the number of correctly identified elements as well as the execution times, comparing the effectiveness and efficiency of VON Similo LLM against the baseline algorithm. In addition, motivations from the LLM were recorded and analyzed for all instances where the original approach failed to find the right web element. VON Similo LLM demonstrated improved performance, reducing failed localizations from 70 to 39 (out of 804), a 44 percent reduction. Despite its slower execution time and additional costs of using the GPT-4 model, the LLMs human-like reasoning showed promise in enhancing web element localization. LLM technology can enhance web element identification in GUI test automation, reducing false positives and potentially lowering maintenance costs. However, further research is necessary to fully understand LLMs capabilities, limitations, and practical use in GUI testing.

ContextManual graphical user interface (GUI) software testing presents a substantial part of the overall practiced testing efforts, despite various research efforts to further increase test automation. Augmented Testing (AT), a novel approach for GUI testing, aims to aid manual GUI-based testing through a tool-supported approach where an intermediary visual layer is rendered between the system under test (SUT) and the tester, superimposing relevant test information.ObjectiveThe primary objective of this study is to gather empirical evidence regarding AT's efficiency compared to manual GUI-based regression testing. Existing studies involving testing approaches under the AT definition primarily focus on exploratory GUI testing, leaving a gap in the context of regression testing. As a secondary objective, we investigate AT's benefits, drawbacks, and usability issues when deployed with the demonstrator tool, Scout.MethodWe conducted an experiment involving 13 industry professionals, from six companies, comparing AT to manual GUI-based regression testing. These results were complemented by interviews and Bayesian data analysis (BDA) of the study's quantitative results.ResultsThe results of the Bayesian data analysis revealed that the use of AT shortens test durations in 70% of the cases on average, concluding that AT is more efficient. When comparing the means of the total duration to perform all tests, AT reduced the test duration by 36% in total. Participant interviews highlighted nine benefits and eleven drawbacks of AT, while observations revealed four usability issues.ConclusionThis study presents empirical evidence of improved efficiency using AT in the context of manual GUI-based regression testing. We further report AT's benefits, drawbacks, and usability issues. The majority of identified usability issues and drawbacks can be attributed to the tool implementation of AT and, thus, can serve as valuable input for future tool development.

Requirements are key artefacts to describe the intended purpose of a software system. The quality of requirements is crucial for deciding what to do next, impacting the development process’ effectiveness and efficiency. However, we know very little about the connection between practitioners’ perceptions regarding requirements quality and its impact on the process or the feelings of the professionals involved in the development process. Objectives: This study investigates: i) How software development practitioners define requirements quality, ii) how the perceived quality of requirements impact process and stakeholders’ well-being, and iii) what are the causes and potential solutions for poor-quality requirements. Method: This study was performed as a descriptive interview study at a sub-organization of a Nordic bank that develops its own web and mobile apps. The data collection comprises interviews with 20 practitioners, including requirements engineers, developers, testers, and newly employed developers, with five interviewees from each group. Results: The results show that different roles have different views on what makes a requirement good quality. Participants highlighted that, in general, they experience negative emotions, more work, and overhead communication when they work with requirements they perceive to be of poor quality. The practitioners also describe positive effects on their performance and positive feelings when they work with requirements that they perceive to be good.

Modern software development is a team-based effort supported by tools, processes, and practices. One integral part is automated testing, where developers incorporate automated tests on multiple levels of system abstraction, from low-level unit tests to high-level system tests and Graphical User Interface (GUI) tests. Furthermore, the common practices of code reviews allow collaboration on artifacts based on discussions that improve the artifact’s quality and to share information within the team. However, the characteristics of GUI-based tests, due to the level of abstraction and visual elements, introduce additional requirements and complexities compared to code or lower-level test code review, delimiting the practice benefits.The objective of this work is to propose a tool-supported workflow that enables active collaboration among stakeholders and improves the efficiency and effectiveness of team-based development of GUI-based tests.To evaluate the workflow, and show proof of concept, a technical demonstrator for merging of GUI-based tests was to be developed. However, during its development, we encountered several unforeseen challenges that forced us to halt its development. We report the negative results from this development and the main challenges we encountered, as well as the rationale and the decisions we took towards this workflow.In conclusion, this work presents a negative research result on a failed attempt to propose a tool-supported workflow that enables active collaboration on GUI-based tests. The outcome and learnings of this work are intended to guide future research and prevent researchers from falling into the same pitfalls we did.

Context Non-functional requirements (NFRs) (also referred to as system qualities) are essential for developing high-quality software. Notwithstanding its importance, NFR testing remains challenging, especially in terms of automation. Compared to manual verification, automated testing shows the potential to improve the efficiency and effectiveness of quality assurance, especially in the context of Continuous Integration (CI). However, studies on how companies manage automated NFR testing through CI are limited. Objective This study examines how automated NFR testing can be enabled and supported using CI environments in software development companies. Method We performed a multi-case study at four companies by conducting 22 semi-structured interviews with industrial practitioners. Results Maintainability , reliability , performance , security and scalability , were found to be evaluated with automated tests in CI environments. Testing practices, quality metrics, and challenges for measuring NFRs were reported. Conclusions This study presents an empirically derived model that shows how data produced by CI environments can be used for evaluation and monitoring of implemented NFR quality. Additionally, the manuscript presents explicit metrics, CI components, tools, and challenges that shall be considered while performing NFR testing in practice.

Fragile (i.e., non-robust) test execution is a common challenge for automated GUI-based testing of web applications as they evolve. Despite recent progress, there is still room for improvement since test execution failures caused by technical limitations result in unnecessary maintenance costs that limit its effectiveness and efficiency. One of the most reported technical challenges for web-based tests concerns how to reliably locate a web element used by a test script.This paper proposes the novel concept of Visually Overlapping Nodes (VON) that reduces fragility by utilizing the phenomenon that visual web elements (observed by the user) are constructed from multiple web-elements in the Document Object Model (DOM) that overlaps visually.We demonstrate the approach in a tool, VON Similo, which extends the state-of-the-art multi-locator approach (Similo) that is also used as the baseline for an experiment. In the experiment, a ground truth set of 1163 manually collected web element pairs, from different releases of the 40 most popular web applications on the internet, are used to compare the approaches' precision, recall, and accuracy.Our results show that VON Similo provides 94.7% accuracy in identifying a web element in a new release of the same SUT. In comparison, Similo provides 83.8% accuracy.These results demonstrate the applicability of the visually overlapping nodes concept/tool for web element localization in evolving web applications and contribute a novel way of thinking about web element localization in future research on GUI-based testing.

The COVID-19 pandemic has had a dramatic effect on society, including teaching within higher education that was forced to adapt to online teaching. Research on this phenomenon has looked at pedagogical methods as well as student perceptions of this way of teaching. However, to the best of our knowledge, no studies have looked at the wider perspective, within the entire student populous of a university, what students’ perceptions are and how these correlate with the students’ previous experiences and habits with online platforms, e.g., online streaming or social media. In this study, we perform a questionnaire survey with 431 responses with students from 20 programs at Blekinge Institute of technology. The survey responses are analyzed using descriptive statistics and qualitative analysis to draw its conclusions. Results show that there is no correlation between previous habits and student experience with online platforms in relation to online learning. Instead, other factors, e.g., teacher engagement, is found central for student learning and therefore important to consider for future research and development of online teaching methodologies.

Review of software artifacts, such as source or test code, is a common practice in industrial practice. However, although review guidelines are available for source and low-level test code, for GUI-based testing artifacts, such guidelines are missing. The goal of this work is to define a set of guidelines from literature about production and test code, that can be mapped to GUI-based testing artifacts. A systematic literature review is conducted, using white and gray literature to identify guidelines for source and test code. These synthesized guidelines are then mapped, through examples, to create actionable, and applicable, guidelines for GUI-based testing artifacts. The results of the study are 33 guidelines, summarized in nine guideline categories, that are successfully mapped as applicable to GUI-based testing artifacts. Of the collected literature, only 10 sources contained test-specific code review guidelines. These guideline categories are: perform automated checks, use checklists, provide context information, utilize metrics, ensure readability, visualize changes, reduce complexity, check conformity with the requirements and follow design principles and patterns . This pivotal set of guidelines provides an industrial contribution in filling the gap of general guidelines for review of GUI-based testing artifacts. Additionally, this work highlights, from an academic perspective, the need for future research in this area to also develop guidelines for other specific aspects of GUI-based testing practice, and to take into account other facets of the review process not covered by this work, such as reviewer selection.