Regression test optimization and prioritization using Honey Bee optimization algorithm with fuzzy rule base

Nayak, Soumen; Kumar, Chiranjeev; Tripathi, Sachin; Mohanty, Nirjharini; Baral, Vishal

doi:10.1007/s00500-020-05428-z

Cited by 10 publications

(4 citation statements)

References 26 publications

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“…Genetic algorithms were also employed to search for an optimal ordering of test cases in another work, 42 which also leveraged history‐based information. Other studies in the field employed nature‐inspired meta‐heuristics, such as ant colony , 43 bee algorithm , 44 fish school 45 and particle swarm , 46 to search for a solution which maximizes different properties, for example, historical metrics or rate of coverage. More recently, prioritization strategies employing machine learning techniques have been investigated.…”

Section: Background and Related Workmentioning

confidence: 99%

Regression test prioritization leveraging source code similarity with tree kernels

Altiero,

Corazza,

Di Martino

et al. 2024

J Software Evolu Process

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Regression test prioritization (RTP) is an active research field, aiming at re‐ordering the tests in a test suite to maximize the rate at which faults are detected. A number of RTP strategies have been proposed, leveraging different factors to reorder tests. Some techniques include an analysis of changed source code, to assign higher priority to tests stressing modified parts of the codebase. Still, most of these change‐based solutions focus on simple text‐level comparisons among versions. We believe that measuring source code changes in a more refined way, capable of discriminating between mere textual changes (e.g., renaming of a local variable) and more structural changes (e.g., changes in the control flow), could lead to significant benefits in RTP, under the assumption that major structural changes are also more likely to introduce faults. To this end, we propose two novel RTP techniques that leverage tree kernels (TK), a class of similarity functions largely used in Natural Language Processing on tree‐structured data. In particular, we apply TKs to abstract syntax trees of source code, to more precisely quantify the extent of structural changes in the source code, and prioritize tests accordingly. We assessed the effectiveness of the proposals by conducting an empirical study on five real‐world Java projects, also used in a number of RTP‐related papers. We automatically generated, for each considered pair of software versions (i.e., old version, new version) in the evolution of the involved projects, 100 variations with artificially injected faults, leading to over 5k different software evolution scenarios overall. We compared the proposed prioritization approaches against well‐known prioritization techniques, evaluating both their effectiveness and their execution times. Our findings show that leveraging more refined code change analysis techniques to quantify the extent of changes in source code can lead to relevant improvements in prioritization effectiveness, while typically introducing negligible overheads due to their execution.

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Section: Background and Related Workmentioning

confidence: 99%

Regression test prioritization leveraging source code similarity with tree kernels

Altiero,

Corazza,

Di Martino

et al. 2024

J Software Evolu Process

View full text Add to dashboard Cite

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“…At the end stage, the HBO algorithm is exploited for optimal hyper parameter adjustment process. A honeybee (HB) colony could fly up to 11 km for exploiting food sources and might attempt to terminate larger areas with large sources of food [23]. The cycle of forager starts with the hunt by scout bee for enticing flower patch.…”

Section: Image Classificationmentioning

confidence: 99%

Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Palaniswamy

Vellingiri

2023

IEEE Access

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Recently, the Internet of Things (IoT) and computer vision technologies find useful in different applications, especially in healthcare. IoT driven healthcare solutions provide intelligent solutions for enabling substantial reduction of expenses and improvisation of healthcare service quality. At the same time, Diabetic Retinopathy (DR) can be described as permanent blindness and eyesight damage because of the diabetic condition in humans. Accurate and early detection of DR could decrease the loss of damage. Computer-Aided Diagnoses (CAD) model based on retinal fundus image is a powerful tool to help experts diagnose DR. Some traditional Machine Learning (ML) based DR diagnoses model has currently existed in this study. The recent developments of Deep Learning (DL) and its considerable achievement over conventional ML algorithms for different applications make it easier to design effectual DR diagnosis model. With this motivation, this paper presents a novel IoT and DL enabled diabetic retinopathy diagnosis model (IoTDL-DRD) using retinal fundus images. The presented Internet of Things Deep Learning -Diabetic Retinopathy Diagnosis (IoTDL-DRD) technique utilizes IoT devices for data collection purposes and then transfers them to the cloud server to process them. Followed by, the retinal fundus images are preprocessed to remove noise and improve contrast level. Next, mayfly optimization based region growing (MFORG) based segmentation technique is utilized to detect lesion regions in the fundus image. Moreover, densely connected network (DenseNet) based feature extractor and Long Short Term Memory (LSTM) based classifier is used for effective DR diagnosis. Furthermore, the parameter optimization of the LSTM method can be carried out by Honey Bee Optimization (HBO) algorithm. For evaluating the improved DR diagnostic outcomes of the IoTDL-DRD technique, a comprehensive set of simulations were carried out. A wide ranging comparison study reported the superior performance of the proposed method. INDEX TERMS Computer aided diagnosis, Deep learning, Diabetic retinopathy, Fundus images, Honey bee optimization.

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“…Worker bees are categorized as Scouts and Foragers. Scouts are supposed to explore new food sources while foragers exploit the already explored paths to reach the food source (6) . To search for available food sources, the Scout bees begin randomly from the hive.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial Bee Colony (ABC) is a well-known technique that is applied to regression TCSP (6) . The BCO algorithm for solving fault-based regression test prioritization within a time-constrained environment was proposed and implemented in (2) respectively.…”

Section: Introductionmentioning

confidence: 99%

Empirical Evaluation of Tetrad Optimization Methods for Test Case Selection and Prioritization

Singhal¹,

Jatana²,

Dhand³

et al. 2023

IJST

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Objectives: Software researchers have been taking advantage of various evolutionary optimization approaches by digitizing them. Test case selection and prioritization based on fault coverage criteria within a time-constrained environment is important in regression testing problem. Methods: This work empirically evaluates different approaches that includes evolutionary approaches (Ant Colony Optimization, Bee Colony Optimization, a combination of Genetic Algorithms and Bee Colony optimization), and a Greedy approach. These tetrad techniques have been successfully applied to regression testing. Also, tools have been developed for their implementation. Eight open-source test programs, written in C language have been used for empirical evaluation of the regression testing approaches. Findings: The accuracy achieved by t-GSC, being a greedy technique, was found to be least; while that of ACO was found to be the best. All the tetrad approaches yielded borderline better or worse results, while all the four gave excellent time and size gains. Novelty: There are many studies available in the literature that compare various regression testing approaches of a similar kind. Instead of repeating the same, it is intended to evaluate two well-accepted approximation approaches: a hybrid approach, and a greedy approach. It has been tried to evaluate the efficiency of the greedy approach with the metaheuristic approach. It is imperative to compare approaches following different algorithmic paradigms, yet trying to solve the same problem.

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Regression test optimization and prioritization using Honey Bee optimization algorithm with fuzzy rule base

Cited by 10 publications

References 26 publications

Regression test prioritization leveraging source code similarity with tree kernels

Regression test prioritization leveraging source code similarity with tree kernels

Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images

Empirical Evaluation of Tetrad Optimization Methods for Test Case Selection and Prioritization

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