What Factors Make SQL Test Cases Understandable for Testers? A Human Study of Automated Test Data Generation Techniques

Kapfhammer, Gregory M.; McMinn, Phil

doi:10.1109/icsme.2019.00076

Cited by 9 publications

(5 citation statements)

References 47 publications

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“…In similar vein, Li et al describe UnitTestScribe (Li et al 2016). Alsharif et al (2019) investigated which factors are important for the understandability of automatically generated SQL schema tests. They saw that human-readable string values are better to understand than randomly generated ones and the repetition between generated test cases made it easier to focus on the relevant differences of the test cases towards each other.…”

Section: Understandability Of Test Casesmentioning

confidence: 99%

Developer-centric test amplification

Brandt

Zaidman

2022

Empir Software Eng

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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"Image missing" 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.

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Section: Understandability Of Test Casesmentioning

confidence: 99%

Developer-centric test amplification

Brandt

Zaidman

2022

Empir Software Eng

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“…Alsharif et al (Alsharif et al, 2019) investigated which factors are important for the understandability of automatically generated SQL schema tests. They saw that human-readable string values are better to understand than randomly generated ones and the repetition between generated test cases made it easier to focus on the relevant differences of the test cases towards each other.…”

Section: Understandability Of Test Casesmentioning

confidence: 99%

Developer-Centric Test Amplification The Interplay Between Automatic Generation and Human Exploration

Brandt,

Zaidman

2021

Preprint

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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.

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“…Other studies have found that readability can be increased by-for example-using real strings instead of random ones (e.g., by pulling string values from documentation), inserting default values for "unimportant" elements (rather than omitting them), and limiting the use and mixture of null values with normal values 11 [11,12].…”

Section: Human-readable Testsmentioning

confidence: 99%

“…Note that high derivative values indicate a pair of very dissimilar output (high numerator) with similar inputs (low denominator), hence revealing sets of input values that are more sensitive to changes in the software behaviour. Going back to our Date example, let us consider the d i and d o for Dates as the edit distance 12 between the inputs and outputs, respectively, when seen as strings (note that valid dates are just printed back as strings on the output side):…”

Section: Finding Input Boundariesmentioning

confidence: 99%

Automated Support for Unit Test Generation: A Tutorial Book Chapter

Fontes¹,

Gay²,

Neto³

et al. 2021

Preprint

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Unit testing is a stage of testing where the smallest segment of code that can be tested in isolation from the rest of the system-often a class-is tested. Unit tests are typically written as executable code, often in a format provided by a unit testing framework such as pytest for Python.Creating unit tests is a time and effort-intensive process with many repetitive, manual elements. To illustrate how AI can support unit testing, this chapter introduces the concept of search-based unit test generation. This technique frames the selection of test input as an optimization problem-we seek a set of test cases that meet some measurable goal of a tester-and unleashes powerful metaheuristic search algorithms to identify the best possible test cases within a restricted timeframe. This chapter introduces two algorithms that can generate pytest-formatted unit tests, tuned towards coverage of source code statements. The chapter concludes by discussing more advanced concepts and gives pointers to further reading for how artificial intelligence can support developers and testers when unit testing software.

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What Factors Make SQL Test Cases Understandable for Testers? A Human Study of Automated Test Data Generation Techniques

Abstract: This is a repository copy of What factors make SQL test cases understandable for testers? A human study of automated test data generation techniques.

Cited by 9 publications

References 47 publications

Developer-centric test amplification

Developer-centric test amplification

Developer-Centric Test Amplification The Interplay Between Automatic Generation and Human Exploration

Automated Support for Unit Test Generation: A Tutorial Book Chapter

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