The adoption of capture-recapture in software engineering

Liu, Gaoxuan; Rong, Guoping; Zhang, He; Qi, Shihua

doi:10.1145/2745802.2745816

Cited by 12 publications

(9 citation statements)

References 9 publications

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“…The same question may arise in the social sciences, when we consider deviant (Böhning et al, 2004) Even when the problem is that of estimating the richness of a population, the max-imum number of classes/species could be of interest in itself or worth using to obtain a more precise estimate, based on both lower and upper bounds for such a quantity; see Eren et al (2012) for an application to Archaeology. A similar aim can also be found in software engineering, where the maximum number of defects in a software application is often of interest, see Liu et al (2015) or, just to give an example, the web page Software Testing Help (2020) containing a discussion of the importance of such information in software testing. In all of these cases, having a reliable estimate of the maximum size of the population of interest could be a crucial step towards providing a timely and successful solution to the problem.…”

Section: Motivationmentioning

confidence: 99%

“…A similar aim can also be found in software engineering, where the maximum number of defects in a software application is often of interest, see Liu et al . (2015) or, just to give an example, the web page Software Testing Help (2020) containing a discussion of the importance of such information in software testing. In all of these cases, having a reliable estimate of the maximum size of the population of interest could be a crucial step toward providing a timely and successful solution to the problem.…”

Section: Motivationmentioning

confidence: 99%

“…Capture-recapture methods were originally developed in the ecological setting with the aim of estimating the unknown size of an (possibly elusive) animal population; since then, they have been gradually applied to other empirical settings, ranging from epidemiology and public health, see Böhning et al (2004), to biodiversity, see Bunge et al (2012), text analysis, see Efron and Thistead (1976), and software engineering, see Liu et al (2015).…”

Section: Introductionmentioning

confidence: 99%

“…(2012), text analysis, see Efron and Thisted (1976), and software engineering, see Liu et al . (2015). An overview on closed population capture‐recapture methods in given by Chao et al .…”

Section: Introductionmentioning

confidence: 99%

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Upper bound estimators of the population size based on ordinal models for capture‐recapture experiments

Alfó

Böhning

Rocchetti³

2020

Biometrics

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Capture-recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health and biodiversity are just a few examples. The estimation of the number of unseen units tigated using several benchmark datasets and a large-scale simulation experiment based on the scheme discussed by Pledger (2005).

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…(2012), text analysis, see Efron and Thisted (1976), and software engineering, see Liu et al . (2015). An overview on closed population capture‐recapture methods in given by Chao et al .…”

Section: Introductionmentioning

confidence: 99%

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Upper bound estimators of the population size based on ordinal models for capture‐recapture experiments

Alfó

Böhning

Rocchetti³

2020

Biometrics

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“…Based on the four basic CRC models, various estimators were developed. According to a recent systematic review [16], MhJK, MhCH, MtCH are the three most frequently investigated and most effective estimators in software engineering. Apart from that, we investigate another two estimators (i.e., M0 and Mth) to ensure all four basic models are investigated.…”

Section: B Predict Total Bugs Using Crcmentioning

confidence: 99%

Effective Automated Decision Support for Managing Crowdtesting

Wang,

Yang,

Krishna

et al. 2018

Preprint

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Crowdtesting has grown to be an effective alternative to traditional testing, especially in mobile apps. However, crowdtesting is hard to manage in nature. Given the complexity of mobile applications and unpredictability of distributed, parallel crowdtesting process, it is difficult to estimate (a) the remaining number of bugs as yet undetected or (b) the required cost to find those bugs. Experience-based decisions may result in ineffective crowdtesting process.This paper aims at exploring automated decision support to effectively manage crowdtesting process. The proposed ISENSE applies incremental sampling technique to process crowdtesting reports arriving in chronological order, organizes them into fixedsize groups as dynamic inputs, and predicts two test completion indicators in an incrementally manner. The two indicators are: 1) total number of bugs predicted with Capture-ReCapture (CRC) model, and 2) required test cost for achieving certain test objectives predicted with AutoRegressive Integrated Moving Average (ARIMA) model. We assess ISENSE using 46,434 reports of 218 crowdtesting tasks from one of the largest crowdtesting platforms in China. Its effectiveness is demonstrated through two applications for automating crowdtesting management, i.e. automation of task closing decision, and semi-automation of task closing tradeoff analysis. The results show that decision automation using ISENSE will provide managers with greater opportunities to achieve cost-effectiveness gains of crowdtesting. Specifically, a median of 100% bugs can be detected with 30% saved cost based on the automated close prediction.1 ISENSE is named considering it likes a sensor in crowdtesting process to raise the awareness of the testing progress.

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False negative of defects estimation in crowdsourced testing

Shi

Zhu

et al. 2022

J Software Evolu Process

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Estimating the population of defects is a key reference for completion evaluation in crowdsourced testing. Current studies commonly use the biological capture–recapture model (CRC model) to estimate the population of defects in crowdsourced tests with good results. However, the lack of consideration of false negatives for defects in existing studies leads to imprecise estimation of completion in crowdsourced testing, which in turn influences decision support during crowdsourced testing. This study analyzes the basic assumptions of the CRC model in biology and maps them to the field of crowdsourced testing, improves the Lincoln–Peterson estimator and sample coverage estimator utilizing false negative assumptions, and compares them with original estimators in 29 real crowdsourced testing projects. The experimental results show that our improved estimators can effectively reduce the estimation relative error of the population of defects caused by false negatives.

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The adoption of capture-recapture in software engineering

Cited by 12 publications

References 9 publications

Upper bound estimators of the population size based on ordinal models for capture‐recapture experiments

Upper bound estimators of the population size based on ordinal models for capture‐recapture experiments

Effective Automated Decision Support for Managing Crowdtesting

False negative of defects estimation in crowdsourced testing

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