An automated approach to estimating code coverage measures via execution logs

Chen, Boyuan; Jian, Song; Xu, Peng; Hu, Xing; Jiang, Zhen Ming

doi:10.1145/3238147.3238214

Cited by 41 publications

(17 citation statements)

References 42 publications

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“…More than 10 years later, Chen et al (2018) propose an automated approach to estimate code coverage via execution logs named LogCoCo. The motivation for this use of log data comes from the need to estimate code coverage from production code.…”

Section: Resultsmentioning

confidence: 99%

Log-based software monitoring: a systematic mapping study

Cândido

Aniche

Deursen

2021

PeerJ Computer Science

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Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve system reliability. However, despite the rich ecosystem around industry-ready log solutions, monitoring complex systems and getting insights from log data remains a challenge. Researchers and practitioners have been actively working to address several challenges related to logs, e.g., how to effectively provide better tooling support for logging decisions to developers, how to effectively process and store log data, and how to extract insights from log data. A holistic view of the research effort on logging practices and automated log analysis is key to provide directions and disseminate the state-of-the-art for technology transfer. In this paper, we study 108 papers (72 research track papers, 24 journals, and 12 industry track papers) from different communities (e.g., machine learning, software engineering, and systems) and structure the research field in light of the life-cycle of log data. Our analysis shows that (1) logging is challenging not only in open-source projects but also in industry, (2) machine learning is a promising approach to enable a contextual analysis of source code for log recommendation but further investigation is required to assess the usability of those tools in practice, (3) few studies approached efficient persistence of log data, and (4) there are open opportunities to analyze application logs and to evaluate state-of-the-art log analysis techniques in a DevOps context.

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Section: Resultsmentioning

confidence: 99%

Log-based software monitoring: a systematic mapping study

Cândido

Aniche

Deursen

2021

PeerJ Computer Science

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“…Selenium IDE is basically a record and playback tool, the test cases generated by Selenium IDE are saved and deployed as JUnit, NUnit test cases. The sample test cases were run through NUnit code coverage [31,32] which achieved the following result over the main modules like performing an insertion and deletion of the records of customers.…”

Section: Experiments and Evaluation Experimentsmentioning

confidence: 99%

Integrated Pairwise Testing based Genetic Algorithm for Test Optimization

Swathi¹,

Tiwari²

2021

IJACSA

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Generation of Test cases in software testing is an important and a complex activity as it deals with diversified range of inputs. Fundamentally, test case generation is considered to be a multi-objective problem as it aims to cover many targets. Deriving test cases for the Web Applications has become critical to the most of the enterprises. In this paper, a solution for generating test cases for web applications is proposed; the solution uses the System Graph (consisting of links and data dependencies) considering that test cases were based on a combination of input values and data dependencies. Pairwise testing is used to derive the test cases to be executing from entire test cases and then a genetic algorithm is proposed to generate test cases specific to functional testing. The proposed approach was tested through two distinct experiments by measuring the code coverage at every generation and results show that genetic algorithm used increased the fitness value and code coverage. Overall, the results of the paper validate the proposed approach and algorithm, having potential in further construct an automated integrated solution for generating test cases for the entire process.

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“…However, we believe that the development of scalable software analytics practices [25] represents an opportunity to include information from the software operations environment in various development activities, including testing [4,6]. For instance, Winter et al [39] recently brought information about the number of times a log statement is executed to the developer's IDE to raise awareness about the load it represented during the operations of the system.…”

Section: Execution Weightsmentioning

confidence: 99%

Commonality-Driven Unit Test Generation

Evers

Derakhshanfar

Devroey

et al. 2020

Search-Based Software Engineering

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Various search-based test generation techniques have been proposed to automate the generation of unit tests fulfilling different criteria (e.g., line coverage, branch coverage, mutation score, etc.). Despite several advances made over the years, search-based unit test generation still suffers from a lack of guidance due to the limited amount of information available in the source code that, for instance, hampers the generation of complex objects. Previous studies introduced many strategies to address this issue, e.g., dynamic symbolic execution or seeding, but do not take the internal execution of the methods into account. In this paper, we introduce a novel secondary objective called commonality score, measuring how close the execution path of a test case is from reproducing a common or uncommon execution pattern observed during the operation of the software. To assess the commonality score, we implemented it in EvoSuite and evaluated its application on 150 classes from JabRef, an open-source software for managing bibliography references. Our results are mixed. Our approach leads to test cases that indeed follow common or uncommon execution patterns. However, if the commonality score can have a positive impact on the structural coverage and mutation score of the generated test suites, it can also be detrimental in some cases.

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An automated approach to estimating code coverage measures via execution logs

Cited by 41 publications

References 42 publications

Log-based software monitoring: a systematic mapping study

Log-based software monitoring: a systematic mapping study

Integrated Pairwise Testing based Genetic Algorithm for Test Optimization

Commonality-Driven Unit Test Generation

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