A Learn-to-Rank Method for Model-Based Regression Test Case Prioritization

Huang, Yechao; Shu, Ting; Ding, Zuohua

doi:10.1109/access.2021.3053163

Cited by 17 publications

(4 citation statements)

References 47 publications

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“…Execution time 10 will be used to measure the performance of the technique in terms of overhead incurred. APFD has been used in studies [33][34][35][36] to measure the effectiveness of TCP techniques. APFD can be calculated by the formula given in Eq.…”

Section: Figure 1 Flow Of Activitiesmentioning

confidence: 99%

An exploratory study of history-based test case prioritization techniques on different datasets

Hassan,

Jawawi,

Ahmad

2024

Baghdad Sci.J

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In regression testing, Test case prioritization (TCP) is a technique to arrange all the available test cases. TCP techniques can improve fault detection performance which is measured by the average percentage of fault detection (APFD). History-based TCP is one of the TCP techniques that consider the history of past data to prioritize test cases. The issue of equal priority allocation to test cases is a common problem for most TCP techniques. However, this problem has not been explored in history-based TCP techniques. To solve this problem in regression testing, most of the researchers resort to random sorting of test cases. This study aims to investigate equal priority in history-based TCP techniques. The first objective is to implement different history-based TCP techniques. The second objective is to explore the problem of equal priority in history-based TCP techniques. The third objective is to explore random sorting as a solution to the problem of equal priority in history-based TCP techniques. Datasets of historical records of test cases from conventional and modern sources were collected. History-based TCP techniques were applied to different datasets. The History-based TCP techniques were checked for the problem of equal priority. Then random sorting was used as a solution to the problem of equal priority. Finally, the results were elaborated in terms of APFD and execution time. The results indicate that history-based techniques also suffer from the problem of equal priority like other types of TCP techniques. Secondly, random sorting does not produce optimal results while trying to solve the problem of equal priority in history-based TCP. Furthermore, random sorting deteriorates the results of history-based TCP techniques when employed to solve the problem of equal priority. One should resort to random sorting if no other solution exists. The decision to choose the best solution requires a cost-benefit analysis keeping in view the context and solution under consideration.

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Section: Figure 1 Flow Of Activitiesmentioning

confidence: 99%

An exploratory study of history-based test case prioritization techniques on different datasets

Hassan,

Jawawi,

Ahmad

2024

Baghdad Sci.J

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“…A new learn-to-rank approach was constructed [22] using the extended finite state machine (EFSM) for TCP. The random forest approach included heuristic prioritizing schemes, but it did not consider [23].…”

Section: Literature Surveymentioning

confidence: 99%

An Improved Deep Learning Based Test Case Prioritization Using Deep Reinforcement Learning

2024

IJIES

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Continuous integration (CI) testing is crucial in modern software engineering and test case prioritization (TCP) techniques improve regression testing (RT) by prioritizing test cases (TCs). Various model has been developed to improve TCs failure prediction and prioritization in CI environments. But, prioritizing the TCs on large test suites without loss of information is a major challenging task. To address this, deep reinforcement prioritizer (DeepRP) model is proposed to improve prioritization in TCP on large test suites. This model employs deep reinforcement learning (DRL) model to learn more test case features, such as changes in source code, version control and code coverage. Also, it enhances self-optimization and adaptive ability for TCP. DRL training employs a deep neural network (DNN) structure to approximate various RL functions like value operation, Q function, transformation system and reward function. An RL system called Q-Learning which determines the appropriate action for an agent based on their action-value role. The DeepRP model uses test case features as input data and the priority of the test case as output. The action includes categorising TCs based on given scores, updating evaluations, calculating reward, and storing the chosen score in a temporary vector among the operations. The reward is computed based on the difference among the specified and ideal rankings for improved TCP on large test suites. The actions include sorting TCs based on assigned scores, updating observations, computing a reward and preserving the selected score in a temporary vector. The reward is calculated based on the distance between the assigned and optimal ranks for better TCP on large test suites. Finally, experimental results show DeepRP significantly achieves RMSE values of 0.09, 0.11 and 0.10 on paint control, IOF/ROL and GSDTSR datasets which is lesser than existing models algorithms like Deepgini, Hansie, DeepOrder, LogTCP and RL-TCP models.

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“…Alongside new methods to represent and extract data, machine learning algorithms have been used in TCP recently because they can learn the rules automatically and therefore becomes more compatible for each project compared to traditional methods. Learning to rank algorithms, which were originally used to rank searching results or prioritize content on websites, was applied to TCP in [13]. In [4], researchers proposed a coverage graph that can utilize both the historical and coverage information of the test case.…”

Section: Related Workmentioning

confidence: 99%

Ensemble approaches for Test Case Prioritization in UI testing

Cao¹,

Vu²,

Le³

et al. 2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

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Test case prioritization, which focuses on ranking test cases, is an important activity in software engineering given a large number of test cases to be executed within a short period of time. Recent approaches use test execution history and test coverage as the key information for ranking prediction while reinforcement learning has the potential for improving the accuracy of prioritization. Still, each approach has its own advantages and limitations. This paper proposes a ensemble method to take advantages of several existing models by combining different them into a single one. We evaluate our ensemble models on the data sets, including sixteen projects. The results show that one of our proposed models outperforms all single models on 12 over 16 data sets.

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A Learn-to-Rank Method for Model-Based Regression Test Case Prioritization

Cited by 17 publications

References 47 publications

An exploratory study of history-based test case prioritization techniques on different datasets

An exploratory study of history-based test case prioritization techniques on different datasets

An Improved Deep Learning Based Test Case Prioritization Using Deep Reinforcement Learning

Ensemble approaches for Test Case Prioritization in UI testing

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