Abstract

Static analysis tools are commonly used in continuous integration (CI) pipelines to detect potential defects without executing the program. Such tools offer configuration options that control how thoroughly the code is analyzed. In SpotBugs, this is done through an effort level setting (Min, Less, More, Max), where higher levels enable deeper and more computationally intensive analysis. In this paper, we study how these effort levels affect energy consumption and analysis results. Our results show that higher effort levels gen- erally consume more energy, which fits our intuition. However, the increase is not uniform: deeper static analysis comes with addi- tional energy cost, but the marginal benefit of higher effort levels may be limited in some cases. This suggests that static analysis tool designers could make energy consumption more transparent and provide configuration options that explicitly expose trade-offs between analysis depth and energy cost.

Abstract

Continuous integration pipelines execute automated tests on ev- ery commit, consuming substantial energy. Batch testing, which groups multiple commits into a single test run, has been shown to reduce test executions in simulation studies, but no prior work has measured whether these reductions translate into actual energy savings. This study measures CPU package energy consumption of CI builds under a baseline run-all approach and two batching strategies (BatchStop4 and linear-4 lwd) across eight open-source Java projects. BatchStop4 achieves energy savings between 57% and 88%, while linear-4 lwd achieves savings between 59% and 94%. Energy savings correlate almost perfectly with time savings (r

0.99), indicating that batching reduces energy primarily by short- ening total execution time. Baseline failure rate strongly predicts achievable savings, while CPU utilisation shows no significant re- lationship. These findings provide empirical evidence that batch testing effectively reduces the environmental footprint of continu- ous integration, particularly for projects with low failure rates.

Abstract

Continuous Integration and Continuous Deployment (CI/CD) have become fundamental to modern software development, with GitHub Actions (GHA) emerging as a dominant automation platform. In this study, we analyze real-world execution records of GHA, examining how developers react to workflow failures, how these workflows are utilized by projects, and how these aspects relate to project characteristics. We quantitatively analyze 258,300 workflow run records from 952 repositories and perform an in-depth qualitative analysis of 21 selected, diverse GitHub repositories to understand how maintainers and contributors interact with workflow results. We identify three distinct failure response patterns, observe that higher usage intensity of GHA workflows correlates with lower failure rates, and uncover a configuration-usage gap where the presence of configuration files masks disabled or unused workflows. Moreover, our qualitative analysis of relationships between project characteristics and utilization patterns yields five hypotheses for future validation.

In this paper we conceptualize the idea of competence debt, created by unreflected use of generative AI. We sketch how competence debt is created and reinforced through three loops and infer recommendations for organizations on how to mitigate it.

Abstract

With generative AI (GenAI) being taken up in many sectors of the economy, especially in software-related areas, there is a growing urgency to understand how GenAI impacts our work. Because GenAI enables us to produce seemingly great outputs with little skill, the impact on learning and skill-building is especially interesting. In this paper, we present a conceptual framework to describe the concept of competence debt—illustrating how extensive use of GenAI can inhibit learning and impact the quality of our outputs. We apply the conceptual framework to describe various already visible impacts of GenAI on organizations and use it to infer guidelines on how organizations can mitigate the negative effects of GenAI usage.

This paper is a collaboration with Exact that resulted from George’s master thesis. We looked into the test flakiness in one of Exact’s systems, highlighting how important meaningful measurement and communication both with the engineers and with management is to address test flakiness.

Abstract

Flaky tests—tests that pass or fail unpredictably even without code changes—undermine the speed and trustworthiness of modern, database-heavy software systems. This study investigates the underlying causes of flakiness at Exact, a large-scale industrial system in which shared database states and resource contention introduced frequent test instability. By repeatedly rerunning the same commit, we pinpointed recurring problem areas and implemented three targeted solutions: (1) minimizing redundant background database tasks, and two strategies for cleaning up “dirty” tests: (2) explicitly disposing of test data and (3) disabling polluting tests with a database sanity check. Together, these interventions raised Exact’s chance of a passing pipeline run from 27% to 95% and boosted their monthly release rate from 60% to 96%. Our findings confirm that rich, systematic measurement and well-prioritized fixes can significantly reduce flakiness in industrial-scale software.

In this paper we mine the first code review round in pull requests to understand whether we can identify developers that follow meaningful patterns.

Abstract

Developers use tools such as GitHub pull requests to review code, discuss proposed changes, and request modifications. While changed files are commonly presented in alphabetical order, this does not necessarily coincide with the reviewer’s preferred navigation sequence. This study investigates the different navigation orders developers follow while commenting on changes submitted in pull requests. We mined code review comments from 23,241 pull requests in 100 popular Java and Python repositories on GitHub to analyze the order in which the reviewers commented on the submitted changes. Our analysis shows that for 44.6% of pull requests, the reviewers comment in a non-alphabetical order. Among these pull requests, we identified traces of alternative meaningful orders: 20.6% (2,134) followed a largest-diff first order, 17.6% (1,827) were commented in the order of the files’ similarity to the pull request’s title and description, and 29% (1,188) of pull requests containing changes to both production and test files adhered to a test-first order. We also observed that the proportion of reviewed files to total submitted files was significantly higher in non-alphabetically ordered reviews, which also received slightly fewer approvals from reviewers, on average. Our findings highlight the need for additional support during code reviews, particularly for larger pull requests, where reviewers are more likely to adopt complex strategies rather than following a single predefined order.

In this paper we surveyed literature that uses qualitative methods in software testing, showing how they span over the different research topics and uncovering areas that are currently undercovered by qualitative research.

Abstract

Software testing research has provided metrics on efficiency, error rates, and insights into the effectiveness of testing methodologies and tools. However, these tell only a part of the story. The qualitative dimension, which studies experiences, perceptions, and decision-making processes is crucial, but less prevalent in literature. This study aims to systematically map qualitative research in software testing to consolidate and categorize the methodologies used in qualitative testing research, highlight their importance, and identify patterns, gaps, and future directions. We conducted a systematic mapping study, identifying and analyzing 102 primary studies from 2003 to 2023. We categorized the studies according to research strategies, data collection, and data analysis methods. We identified case studies and grounded theory as the most prevalent research strategies. Researchers primarily used semi-structured interviews and thematic analysis to understand how practitioners work and gather stakeholder perspectives. The subject areas most covered by qualitative studies included software testing processes and risks, and test automation. Areas such as test oracles, and machine learning were underrepresented. We also assessed the quality of reporting and the methodological rigor, emphasizing the challenges and limitations identified during the process. Through this study, we provide a comprehensive overview of qualitative research practices in software testing, revealing trends, gaps, and methodological insights.

This paper introduces a novel way to look at code coverage in software testing: can we predict it based on the code? Our idea is to learn from existing code coverage in open source projects what characterizes code that developers find it worth to cover with tests.

Abstract

To measure and improve the strength of test suites, software projects and their developers commonly use code coverage and aim for a threshold of around 80%. But what is the 80% of the source code that should be covered? To prepare for the development of new, more refined code coverage criteria, we introduce a novel predictive problem in software testing: whether a code line is, or should be, covered by the test suite. In this short paper, we propose the collection of coverage information, source code metrics, and abstract syntax tree data and explore whether they are relevant to predict whether a code line is exercised by the test suite or not. We present a preliminary experiment using four machine learning (ML) algorithms and an open source Java project. We observe that ML classifiers can achieve high accuracy (up to 90%) on this novel predictive problem. We also apply an explainable method to better understand the characteristics of code lines that make them more “appealing” to be covered. Our work opens a research line worth to investigate further, where the focus of the prediction is the code to be tested. Our innovative approach contrasts with most predictive problems in software testing, which aim to predict the test case failure probability.

Software quality is crucial, but previous research showed that developers often lack awareness about QA practices in their projects. We aimed to address this gap by creating a dashboard that provides insights into various QA activities.

In this work:

• We designed RepoInsights, a dashboard that gives an overview of QA practices like testing, code reviews, automated workflows, and project guidelines.
• We conducted interviews with 14 software engineers from open-source, industry, and academia to evaluate the tool.
• We asked participants to assess real projects using RepoInsights and provide feedback on its usefulness.

Key Findings

• Most participants found value in having a centralized overview of QA metrics across their projects.
• The dashboard helped engineers reflect on QA practices by providing meaningful metrics and details about project activities.
• Participants expressed interest in using such a tool in the future, especially for periodic project health checks.
• Engineers wanted more granular, contextualized information tailored to each project’s specific needs and practices.
• There’s a need for actionable insights derived from the data, not just raw metrics.

Recommendations for Improvement

Based on participant feedback, future QA analytics tools should:

1. Provide highly detailed, project-specific information
2. Offer meaningful comparisons between similar projects
3. Include more context about each project’s unique situation
4. Generate actionable insights and improvement suggestions
5. Allow customization of displayed information
6. Integrate seamlessly into existing developer workflows

Why It Matters

This research lays the groundwork for better QA analytics tools. By improving developers’ awareness and understanding of QA practices, we can potentially enhance software quality across the industry.

In this paper we conduct a large-scale open source contribution study, submitting pull requests with amplified tests to Java projects on GitHub. We analyze the feedback from the maintainers, as well as the preparations we needed to do to prepare the amplified tests for the pull requests. Based on this we collect guidelines on what developers can expect to change in amplified tests before including them in their test suite. Part of this are very helpful changes that are based on the developer’s understanding of the code base and their software project, leading us to call for more support for developers to understanding amplified tests instead of focusing on further automation.

Abstract

Test amplification makes systematic changes to existing, manually written tests to provide tests complementary to an automated test suite. We consider developer-centric test amplification, where the developer explores, judges and edits the amplified tests before adding them to their maintained test suite. However, it is as yet unclear which kind of selection and editing steps developers take before including an amplified test into the test suite. In this paper we conduct an open source contribution study, amplifying tests of open source Java projects from GitHub. We report which deficiencies we observe in the amplified tests while manually filtering and editing them to open 39 pull requests with amplified tests. We present a detailed analysis of the maintainer’s feedback regarding proposed changes, requested information, and expressed judgment. Our observations provide a basis for practitioners to take an informed decision on whether to adopt developer-centric test amplification. As several of the edits we observe are based on the developer’s understanding of the amplified test, we conjecture that developer-centric test amplification should invest in supporting the developer to understand the amplified tests.

Keywords

Software Testing · Automatic Test Generation · Developer-Centric Test Amplification

This paper is a collaboration with Mozilla, where we try out whether we can use fuzzers to automatically generate partial input-tests that developers then complete into full functional tests with assertions. Inspired by the feedback from developers on the initial Bugzilla reports we submitted, we then dive deeper into which kind of coverage gaps are actually relevant for developers to close.

Abstract

Can fuzzers generate partial tests that developers find useful enough to complete into functional tests (e.g., by adding assertions)? To address this question, we develop a prototype within the Mozilla ecosystem and open 13 bug reports proposing partial generated tests for currently uncovered code. We found that the majority of the reactions focus on whether the targeted coverage gap is actually worth testing. To investigate further which coverage gaps developers find relevant to close, we design an automated filter to exclude irrelevant coverage gaps before generating tests. From conversations with 13 developers about whether the remaining coverage gaps are worth closing when a partially generated test is available, we learn that the filtering indeed removes clearly non- test-worthy gaps. The developers propose a variety of additional strategies to address the coverage gaps and how to make fuzz tests and reports more useful for developers.

This paper is based on Khalid’s Master Thesis, where he investigated how usable tests generated by GitHub Copilot are. We observe that the usability is low, meaning that the developers need to make changes to most generated tests. Less changes are needed when there are already tests existing in the same class and the generated test closely mimics the structure of these existing tests.

Abstract

Writing unit tests is a crucial task in software development, but it is also recognized as a time-consuming and tedious task. As such, numerous test generation approaches have been proposed and investigated. However, most of these test generation tools produce tests that are typically difficult to understand. Recently, Large Language Models (LLMs) have shown promising results in generating source code and supporting software engineering tasks. As such, we investigate the usability of tests generated by GitHub Copilot, a proprietary closed-source code generation tool that uses an LLM. We evaluate GitHub Copilot’s test generation abilities both within and without an existing test suite, and we study the impact of different code commenting strategies on test generations. Our investigation evaluates the usability of 290 tests generated by GitHub Copilot for 53 sampled tests from open source projects. Our findings highlight that within an existing test suite, approximately 45.28% of the tests generated by Copilot are passing tests; 54.72% of generated tests are failing, broken, or empty tests. Furthermore, if we generate tests using Copilot without an existing test suite in place, we observe that 92.45% of the tests are failing, broken, or empty tests. Additionally, we study how test method comments influence the usability of test generations.

In this paper we compare a user-driven guided approach of test amplification to the original open test amplification approach. While the user guidance makes it easier for the developers to understand the amplified tests, and it fits better into their typical workflow of writing tests in conjunction with production code, there are also trade-offs. For example, that now we create the expectation of being able to generate tests for the selected branch, while it can be computationally expensive to find the right branch conditions and initialize the objects under test accordingly.

Abstract

Test amplification generates new tests by mutating existing, developer-written tests and keeping those tests that improve the coverage of the test suite. Current amplification tools focus on starting from a specific test and propose coverage improvements all over a software project, requiring considerable effort from the software engineer to understand and evaluate the different tests when deciding whether to include a test in the maintained test suite. In this paper, we propose a novel approach that lets the developer take charge and guide the test amplification process towards a specific branch they would like to test in a control flow graph visualization. We evaluate whether simple modifications to the automatic process that incorporate the guidance make the test amplification more effective at covering targeted branches. In a user study and semi-structured interviews we compare our user-guided test amplification approach to the state-of-the-art open test amplification approach. While our participants prefer the guided approach, we uncover several trade-offs that influence which approach is the better choice, largely depending on the use case of the developer.

Keywords

Software Testing · Test Amplification · Automated Test Code Modification · User-centric Design · Human-Automation Interaction

In this paper we took a look at how we should build visualizations that let developers compare one test case to the rest of a test suite.

The paper was accepted at VISSOFT’22, the 10th IEEE Working Conference on Software Visualization.

Short video introducing our visualization: https://youtu.be/R5KDR_viFRA
Artifact with our implementation, replication documentation and the full codes elicited during the study: https://zenodo.org/record/7022610 We received the award for the best VISSOFT’22 artifact at ICSME’s ROSE festival.

Abstract

Developer testing, the practice of software engineers programmatically checking that their own components behave as they expect, has become the norm in today’s software projects. With the constantly growing size and complexity of software projects and with the rise of automated test generation tools, understanding a test case is becoming more and more important compared to writing test cases from scratch. This holds especially in the area of developer-centric test amplification, where a tool automatically generates new test cases to improve a developer-maintained test suite. To investigate how visualization can help developers understand and judge test cases, we present the TestImpactGraph, a visualization of the call tree and coverage impact of a JUnit test case proposed for amplification. It empowers the developer to drill down into the behavior of a test case, as well as providing them a clear view on how the proposed test case contributes to the coverage of the overall test suite. In a think-aloud study we investigate which information developers seek from the TestImpactGraph, how its features can support them in accessing this information, and observations regarding the coverage impact of test cases. We infer ten actionable recommendations on how developer tests can be visualized to help developers understand their behavior and impact.

Keywords

Software Testing · Test Amplification · Test Review · Test Visualization · Test Understanding

This paper explores which factors make developer-centric test amplification—where generated test cases are taken over into the maintained test suite—
a useful tool for developers.
Short answer: consider and design the interaction of developer & test amplification tool.
Details: have a look at the paper for those 😉!

Abstract

Automatically generating test cases for software has been an active research topic for many years. While current tools can generate powerful regression or crash-reproducing test cases, these are often kept separately from the maintained test suite. In this paper, we leverage the developer’s familiarity with test cases amplified from existing, manually written developer tests. Starting from issues reported by developers in previous studies, we investigate what aspects are important to design a developer-centric test amplification approach, that provides test cases that are taken over by developers into their test suite. We conduct 16 semi-structured interviews with software developers supported by our prototypical designs of a developer-centric test amplification approach and a corresponding test exploration tool. We extend the test amplification tool DSpot, generating test cases that are easier to understand. Our IntelliJ plugin TestCube empowers developers to explore amplified test cases from their familiar environment. From our interviews, we gather 52 observations that we summarize into 23 result categories and give two key recommendations on how future tool designers can make their tools better suited for developer-centric test amplification.

Keywords

Software Testing · Test Amplification · Test Exploration · Test Generation · Developer-Centric Design

This paper discusses a plugin that brings information from the CI to the developer’s IDE to help them fix failing tests faster. It is based on Casper Boone’s excellent Master Thesis, who I had the pleasure to supervise in 2020/21.

The paper was accepted at ICPC’22, the 30th IEEE/ACM International Conference on Program Comprehension (DOI).

Abstract

The most common reason for Continuous Integration (CI) builds to break is failing tests. When a build breaks, a developer often has to scroll through hundreds to thousands of log lines to find which test is failing and why. Finding the issue is a tedious process that relies on a developer’s experience and increases the cost of software testing. We investigate how presenting different kinds of contextual information about CI builds in the Integrated Development Environment (IDE) impacts the time developers take to fix a broken build. Our IntelliJ plugin TestAxis surfaces additional information such as a unique view of the code under test that was changed leading up to the build failure. We conduct a user experiment and show that TestAxis helps developers fix failing tests 13.4% to 48.6% faster. The participants found the features of TestAxis useful and would incorporate it in their development workflow to save time. With TestAxis we set an important step towards removing the need to manually inspect build logs and bringing CI build results to the IDE, ultimately saving developers time.

Keywords

Software Testing · Continuous Integration · Developer Assistance · IDE Plugin · User Experiment

This paper proposes minimization approach for amplified test cases based on first drastically then step by step more conservatively removing statements while checking that the improvement in mutation score remains the same.

The paper was accepted at the NIER track of SCAM’21, the IEEE International Working Conference on Source Code Analysis and Manipulation.

Abstract

Test amplification generates new tests by modifying existing, manually written tests. Up until now, this process preserves statements that were relevant for the original test case but are no longer needed for the behavior of the new test case. These unnecessary statements impact the readability of the tests in question. As a part of the effort to make amplified test cases more readable, we investigate dynamic slicing, taint analysis and static analysis as approaches to remove redundant statements. We design and evaluate a static analysis approach that we implemented as part of the test amplification tool DSpot. Our empirical evaluation on 274 amplified test cases shows that the implemented approach works well: while being rudimentary, it is able to remove a significant portion of the redundant statements in the amplified test cases. While the removal of the statements themselves is fast, verifying that the tests still work as intended through mutation testing is still resource-intensive.

This paper proposes a novel naming approach for amplified test cases based on the methods where the tests provides additional coverage compared to the existing test suite.

The paper was accepted at the NIER track of SCAM’21, the IEEE International Working Conference on Source Code Analysis and Manipulation.

Abstract

Test amplification generates new test cases that improve the coverage of an existing test suite. To convince developers to integrate these new test cases into their test suite, it is crucial to convey the behavior and the improvement in coverage that the amplified test case provides. In this paper, we present NATIC, an approach to generate names for amplified test cases based on the methods they additionally cover, compared to the existing test suite. In a survey among 16 participants with a background in Computer Science, we show that the test names generated by NATIC are valued similarly to names written by experts. According to the participants, the names generated by NATIC outperform expert-written names with respect to informing about coverage improvement, but lack in conveying a test’s behavior. Finally, we discuss how a restriction to two mentioned methods per name would improve the understandability of the test names generated by NATIC.

A dataset of continuous integration log files from a variety of 80 GitHub repositories and the chunk of the file that states why the build failed.

The paper was accepted at the dataset track of MSR’20, the 17th IEEE/ACM International Conference on Mining Software Repositories.

Abstract

Build logs are textual by-products that a software build process creates, often as part of its Continuous Integration (CI) pipeline. Build logs are a paramount source of information for developers when debugging into and understanding a build failure. Recently, attempts to partly automate this time-consuming, purely manual activity have come up, such as rule- or information-retrieval-based techniques.

We believe that having a common data set to compare different build log analysis techniques will advance the research area. It will ultimately increase our understanding of CI build failures. In this paper, we present LogChunks, a collection of 797 annotated Travis CI build logs from 80 GitHub repositories in 29 programming languages. For each build log, LogChunks contains a manually labeled log part (chunk) describing why the build failed. We externally validated the data set with the developers who caused the original build failure.

The width and depth of the LogChunks data set are intended to make it the default benchmark for automated build log analysis techniques.

Keywords

Continuous Integration · Build Log Analysis · Build Failure · Chunk Retrieval