Precise Learn-to-Rank Fault Localization Using Dynamic and Static Features of Target Programs

Kim, Yun‐Ho; Mun, Seokhyeon; Yoo, Shin; Kim, Moonzoo

doi:10.1145/3345628

Cited by 34 publications

(20 citation statements)

References 69 publications

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“…Then, the software modules with more faults are assigned higher test priority. Kim et al [8] also used the LTR technique to precise fault localization for software testing. Their method uses genetic programming to learn a ranking model based on the software's dynamic and static characteristics.…”

Section: B Learn To Rankmentioning

confidence: 99%

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

2021

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Regression testing plays an indispensable role in software maintenance, which refers to retest the software following modifications to determine whether the changes have introduced new faults. However, regression testing requires massive amounts of effort to achieve a high fault detection rate. To address this issue, the test case prioritization technique is used to improve the fault detection rate by adjusting the execution order of test cases. For model-based regression test case prioritization, existing approaches have been developed using the single aspect of model-related information extracted from the previous executed test cases. In this paper, a novel learn-to-rank technique is proposed to prioritize test cases by combining the multidimensional features of Extended Finite State Machine (EFSM) under test to improve fault detection rate. Specifically, our method utilizes the random forest algorithm to combine multiple existing heuristic prioritization methods. Detailed experiments are conducted to evaluate the proposed method's performance in terms of Average Percentage Fault Detected (APFD). The experimental results show that the mean APFD value of our method reaches 0.884 for five subject EFSMs, which is 33.9% higher than the compared methods. INDEX TERMS regression testing, model based testing, EFSM, test case prioritization, learn to rank

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Section: B Learn To Rankmentioning

confidence: 99%

“…We attempt to propose a method that considers multiple characteristics of EFSM to rank test cases. Recently, Kim et al [8]…”

Section: Our Approach a Learn To Rank Test Suite (Ltr-ts)mentioning

confidence: 99%

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

2021

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“…The techniques like MULTRIC [18], TraPT [19], FLUCCS [20], and PRINCE [21] have demonstrated that Learning-to-Rank methods can assist in the identification of fault statements by using a variety of fault-diagnostic characteristics of varying dimensions. Limited in its ability to automatically choose strong preexisting features and find new advanced features for fault localization, it may not fully use the training data information gathered.…”

Section: Literature Reviewmentioning

confidence: 99%

Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

Alhumam

2021

Sensors

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The automatic localization of software faults plays a critical role in assisting software professionals in fixing problems quickly. Despite various existing models for fault tolerance based on static features, localization is still challenging. By considering the dynamic features, the capabilities of the fault recognition models will be significantly enhanced. The current study proposes a model that effectively ranks static and dynamic parameters through Aggregation-Based Neural Ranking (ABNR). The proposed model includes rank lists produced by self-attention layers using rank aggregation mechanisms to merge them into one aggregated rank list. The rank list would yield the suspicious code statements in descending order of the rank. The performance of ABNR is evaluated against the open-source dataset for fault prediction. ABNR model has exhibited noticeable performance in fault localization. The proposed model is evaluated with other existing models like Ochiai, Fault localization technique based on complex network theory, Tarantula, Jaccard, and software-network centrality measure concerning metrics like assertions evaluated, Wilcoxon signed-rank test, and Top-N.

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“…Loyola et al [80] introduced a learning-to-rank-based model to support bug localization. Kim et al [81] presented a learning-to-rank fault localization technique that uses genetic programming to combine static and dynamic features. Sohn et al [20], [82] introduced a learn-to-rank fault localization approach that learns how to rank program elements based on spectrum-based fault localization formulas, code metrics and change metrics.…”

Section: Related Workmentioning

confidence: 99%

An experience-based recommendation system to support migrations of Android applications from Java to Kotlin

Mateus,

Martinez,

Kolski

2021

Preprint

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In 2017, Google announced Kotlin as an official Android programming language, and more recently, as the preferred programming language to build applications. These facts motivated developers to migrate their applications, which is challenging because each migrated piece of code must be tested after the migration to ensure it preserves the expected behavior. Due to the interoperability between Java and Kotlin, most developers decided to migrate their applications gradually. Thus, developers have to decide which file(s) to migrate first on each migration step. However, there are no tools available to help developers make these choices. This paper presents an approach to support a gradual migration of Android applications that given a version of an application written in Java and eventually, in Kotlin, it suggests the most convenient files to migrate. To this end, we built a large-scale corpus of open-source projects that migrated Java files to Kotlin. Then, we trained a learning to rank model using the information extracted from these projects. To validate our model, we verify whether these recommendations made by them correspond to real migrations. The results showed our approach modestly outperforms random approaches. Since most Android applications are written in Java, we conclude that our approach may significantly impact Android applications' development. Therefore, we consider this result is the first step into long-term research towards a model capable of predicting precisely file-level migration, establishing the initial baseline on file migrations.

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Precise Learn-to-Rank Fault Localization Using Dynamic and Static Features of Target Programs

Cited by 34 publications

References 69 publications

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

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

Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection

An experience-based recommendation system to support migrations of Android applications from Java to Kotlin

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