Details of a Researcher - Takada, Shingo

Papers - Takada, Shingo

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Semantic Matching Based Test Case Reuse for Android Applications

Ngoc Bao Nguyen, Shingo Takada

Eighth International Workshop on Validation, Analysis, and Evolution of Software Tests (VST 2025) 174 - 181 2025.03

Research paper (international conference proceedings), Joint Work, Last author, Accepted

Lowering the Barrier of Machine Learning: Achieving Zero Manual Labeling in Review Classification Using LLMs

Yejian Zhang, Shingo Takada

2025 11th International Conference on Computing and Artificial Intelligence (ICCAI 2025) 2025.03

Research paper (international conference proceedings), Joint Work, Last author, Accepted

静的解析ツールが示す優先度は開発者の役に立つのか？

名倉正剛, 尾原秀登, 高田眞吾, 末次健太郎, 中川岳, 浅原明広

電子情報通信学会論文誌 J107-D ( 2 ) 77 - 81 2024.02

Research paper (scientific journal), Accepted

Access to Document (DOI)

RSUTT: Robust Search Using T-Way Testing

Matsukawa C., Takada S.

Communications in Computer and Information Science 2178 CCIS 35 - 50 2024

ISSN 18650929

Recent years have seen an increase of decision-making software based on Machine Learning (ML). Multiple cases have been reported where such software are discriminatory based on attributes such as race and gender. Thus, ML-based decision making software need to be tested for discrimination, or fairness testing. One popular approach to fairness testing is to find discriminatory data items by first conducting a global search, and then searching locally near the found discriminatory data items. Aequitas, CGFT, and KOSEI are three examples taking this approach. However, there are issues in terms of stability and efficiency. We thus propose an approach called Robust Search Using T-way Testing (RSUTT), which is based on CGFT for global search and KOSEI for local search. Experiments showed that RSUTT performs more efficiently compared to Aequitas, CGFT, and KOSEI.

Access to Document (DOI)

Software defect prediction based on JavaBERT and CNN-BiLSTM

Kun Cheng, Shingo Takada

Proc. of 11th International Workshop on Quantitative Approaches to Software Quality (QuASoQ 2023) 3612 51 - 59 2023.12

Research paper (international conference proceedings), Joint Work, Last author, Accepted, ISSN 16130073

Software defects can lead to severe issues in software systems, such as software errors, security vulnerabilities, and decreased software performance. Early prediction of software defects can prevent these problems, reduce development costs, and enhance system reliability. However, existing methods often focus on manually crafted code features and overlook the rich semantic and contextual information in program code. In this paper, we propose a novel approach that integrates JavaBERT-based embeddings with a CNN-BiLSTM model for software defect prediction. Our model considers code context and captures code patterns and dependencies throughout the code, thereby improving prediction performance. We incorporate Optuna to find optimal hyperparameters. We conducted experiments on the PROMISE dataset, which demonstrated that our approach outperforms baseline models, particularly in leveraging code semantics to enhance defect prediction performance.

Review Classification Based on Machine Learning: Classifying Game User Reviews

Zhang Yejian, Shingo Takada

IEEE Access (IEEE) 11 142447 - 142463 2023.12

Research paper (scientific journal), Joint Work, Last author, Accepted

With the development of the game industry, the maturity of online game sales platforms, and the increasing complexity of game software itself, game companies need to analyze massive amounts of user reviews to understand the hidden defects of the game and the direction of future iterations. Manually reading game reviews is a labor-intensive and time-consuming task, as the number of reviews can go up to several thousand per day. Automatically classifying these game reviews will help alleviate this issue, but traditional classifiers will need a large number of labeled instances for training. In this paper, we propose and implement an approach that combines transfer learning in natural language processing (BERT), unsupervised learning, and active learning to classify game reviews using only a small amount of labeled instances. We found that our approach obtains 88.8% classification accuracy with only 100 labeled training instances. Our implementation can be extended to handle different types of new games by using a small amount of extra labeled instances and manual work.

Access to Document (DOI)

Applying Reinforcement Learning for Automated Testing of Mobile Application Focusing on State Definition, Reward, and Learning Method

Keita Murase, Shingo Takada

Proc. of 35th International Conference on Software Engineering & Knowledge Engineering 2023-July 64 - 69 2023.07

Research paper (international conference proceedings), Joint Work, Last author, Accepted, ISSN 23259000

There have been various studies on the automation of mobile app testing. Typical methods for automated testing of mobile apps are based on random search and on building state transition models. But there are problems in terms of the efficiency of search and accuracy of model building. This paper focuses on applying reinforcement learning to testing of mobile apps, especially issues such as explosion of the number of states, fixed rewards for transitions, and difficulty in convergence of learning. We focus on state definition, reward function, and a learning method to solve these problems. Specifically, we define states using discrete values of UI (User Interface) information on the screen, def ine adynamic reward function, and perform periodic learning by using the transition history. The proposed method is implemented and evaluated. Evaluation results show that our proposed approach shows 1.21 times higher coverage than an existing tool using reinforcement learning.

Access to Document (DOI)

Applying Symbolic Execution to Semantic Code Clone Detection

Kazusa Takemoto, Shingo Takada

Proc. of 35th International Conference on Software Engineering & Knowledge Engineering 2023-July 118 - 122 2023.07

Research paper (international conference proceedings), Joint Work, Last author, Accepted, ISSN 23259000

Many approaches have been proposed to detect code clones, which are basically similar code fragments. Most approaches are based on textual similarity. These approaches cannot detect semantic code clones, which are clones that have the same functionality but implemented with different syntax. Two functions can be considered to have the same functionality, when the output is the same given the same input. In order to appropriately generate inputs, we propose applying symbolic execution to semantic code clone detection. These functions are executed to obtain outputs, which are compared to determine if function pairs are clones. Our approach also does not limit output to return values; we also handle arrays and pointers as output, as the execution of the function may cause changes in their values. Furthermore, we classify types to enable cases where the types of inputs and/or outputs are not exactly the same. We evaluate our approach on SemanticCloneBench.

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OSS プロジェクトを対象にしたコーディング規約違反発生状況の分析

名倉正剛, 末次健太郎, 高田眞吾, 尾原秀登, 角幸一郎, 浅原明広

コンピュータソフトウェア (日本ソフトウェア科学会) 40 ( 1 ) 57 - 82 2023.01

Research paper (scientific journal), Joint Work, Accepted

ソフトウェア開発において，開発者が守るべきと考えるようなコーディングスタイルに関する基準(コーディング規約)が存在する．しかし，実際の開発プロジェクトで必ずしも遵守されているとは限らない．我々は，OSSの1,000 プロジェクトのリポジトリを対象に，C++向けコーディング規約準拠検査ツールCpplintに実装された 87 種類のコーディング規約に対するコーディング規約違反の発生と，発生した違反へのリファクタリング等による対処の状況を調査した．その結果，多くのプロジェクトで開発者が「あたりまえ」に守っており，あまり違反が発生せずに発生しても対処される傾向が強い規約と，逆に多くのプロジェクトで開発者にあまり意識されず違反が多く発生し，発生した違反に対しても無視される傾向が強い規約が存在することを確認した．また，遵守の傾向が開発期間内で変化するかどうかを調査したところ，IDEにより自動修正が行われる一部の規約については，プロジェクトの特定期間で遵守の傾向が変化するプロジェクト数が，他の規約とは有意に異なり多いことを確認した．それらの規約については，開発者により守るべきという共通認識が形成されていない状況において，IDEの自動修正により開発者の意図と関係なく結果的に遵守している可能性があることを示唆している．

CC2020 Visualization Tool

Clear A., Cuadros-Vargas E., Takada S.

SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V.2 (SIGCSE 2022 - Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V.2) 1063 - 1064 2022.03

A significant part of the recently published ACM and IEEE-CS report "Computing Curricula 2020: Paradigms for Global Computing Education"[1] was the development of visualizations of any computing degree and the comparison with the ACM and IEEE-CS approved curricula. This visualization covers discipline areas that have approved curricula: Computer Engineering, Computer Science, Software Engineering, Information Systems, Information Technology and Cyber Security. The CC2020 report developed a "Landscape of Computing Knowledge"table (see table 1) aggregating all the topic areas of all six approved curricula into 34 topic areas and then assigned a minimum and maximum value for each of topic areas. As part of the visualizations project an online application has been developed (see Figure 1) where stakeholders can assign a minimum and maximum value to each of the topic areas that are required in their degree program and then that program can be matched against the current ACM/IEEE-CS approved curricula and other degree programs globally. This will have significant importance for educators and other stakeholders of computing degree programs.

Access to Document (DOI)

Fault Localization in Server-Side Applications Using Spectrum-Based Fault Localization

Yoshitomo Sha, Masataka Nagura, Shingo Takada

Proc.of 5th Workshop on Validation, Analysis and Evolution of Software Tests (VST2022) (IEEE) 1128 - 1135 2022.03

Research paper (international conference proceedings), Joint Work, Accepted

Today's software has a very complex structure with multiple components, making it difficult to identify the cause of a fault. The process of identifying the cause of a fault may include referring to the logs from the system if they exist. But large and complex systems may generate a huge amount of logs, making the task of finding the important log messages to be a tedious task. In the case of systems that require continuous operation, the cause of faults must be identified quickly in an efficient manner. In this paper, we propose a method that identifies the log messages that are key for finding faults in server-side applications that has a tiered structure, such as LAMP (Linux, Apache, MySQL, PHP), and outputs logs (including traces during operation). The key part of our proposed approach is the application of Spectrum-Based Fault Localization (SBFL) to log files.

Access to Document (DOI)

Fairness Testing of Machine Learning Software through a Combinatorial Approach

Daniel Perez Morales, Takashi Kitamura, and Shingo Takada

電子情報通信学会知能ソフトウェア工学研究会信学技報 (電子情報通信学会) KBSE2021-50 54 - 59 2022.03

Research paper (conference, symposium, etc.), Joint Work

An efficient discrimination discovery method for fairness testing

Sano S., Kitamura T., Takada S.

Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE) 200 - 205 2022

ISSN 23259000

With the increasing use of machine learning software in our daily life, software fairness has become a growing concern. In this paper, we propose an individual fairness testing technique called KOSEI. Individual fairness is one of the central concepts in software fairness. Testing individual fairness aims to detect individual discriminations included in the software. KOSEI is based on AEQUITAS by Udeshi et al., a landmark fairness testing technique featuring a two-step search strategy of global and local search. KOSEI improves the local search part of AEQUITAS, based on our insight to overcome the limitations of the local search of AEQUITAS. Our experiments show that KOSEI outperforms AEQUITAS by orders of magnitude. KOSEI, on average, detects 5,084.8% more discriminations than AEQUITAS, in just 7.5% of the execution time.

Access to Document (DOI)

Comparing Global Curricula and Local Computing Degree programs using the CC2020 Curriculum Visualization Tool

Clear A., Clear T., Takada S., Cuadros-Vargas E.

Proceedings - Frontiers in Education Conference, FIE (Proceedings - Frontiers in Education Conference, FIE) 2022-October 2022

ISSN 15394565

This special session will introduce the participants to the CC2020 Visualization Tool based on the Landscape of Computing Knowledge Table and its 34 topics areas. It will guide them through the process of assigning a minimum and maximum value to the discipline areas of their own degree programme, then enter them into the application and allow the participants to see where in the landscape of computing their own program fits. It will also allow participants to compare their own programme with any other programme entered into the application. We hope to populate the database with programmes from all over the world over the next few months to ensure a global reach for comparison. There has never been a tool to compare computing degrees and this tool not only allows for comparison with current approved curricula but also allows participants to compare their degree with each other. This tool also enables employers to develop a guideline for potential employees and compare with job definitions to help with alignment of qualifications and competencies.

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Towards Overcoming Type Limitations in Semantic Clone Detection

Leone F., Takada S.

Proceedings - 2022 IEEE 16th International Workshop on Software Clones, IWSC 2022 (Proceedings - 2022 IEEE 16th International Workshop on Software Clones, IWSC 2022) 25 - 31 2022

Currently available tools for semantic clone detection have serious type limitations related to objects, in particular related to instantiation and comparison. The trend in the recent research has focused on improving the performance rather than removing these constraints. This paper proposes a test-based approach for semantic clone detection in Java which overcomes these problems. The test-based approach consists in running two methods with the same input and comparing their outputs. For object instantiation, the approach uses EvoSuite, an automatic JUnit test generator, to create the tests with correct instantiations of all the needed classes, which are then modified to make them suitable for semantic clone detection. The output comparison uses the DeepHash function to obtain numerical values for objects, considering the values of each instance variable. The approach was evaluated with SemanticCloneBench. The results show that the proposed approach performs very well in terms of precision, achieving 94.4%, which is about 8% higher than the reported precision of SLACC, a recent semantic clone detector. Furthermore, out of 228 methods used for the evaluation, the approach was not able to perform the detection for only ten methods.

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SBFL 手法の適用によりサーバサイドアプリケーションの障害原因を特定する手法

謝佳智, 名倉正剛, 高田眞吾

情報処理学会ソフトウェア工学研究会研究報告 (情報処理学会) 2021-SE-209 ( 5 ) 1 - 7 2021.11

Research paper (conference, symposium, etc.)

定量的モデル検査手法Model Measuringの実装に関する研究

小島海, 高田眞吾

情報処理学会ソフトウェア工学研究会研究報告 (情報処理学会) 2021-SE-209 ( 10 ) 1 - 8 2021.11

Research paper (conference, symposium, etc.), Joint Work

Which Factors Affect Q-Learning-based Automated Android Testing - A Study Focusing on Algorithm, Learning Target, and Reward Function -

Yuki Moriguchi, Shingo Takada

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering (SEKE2021) (Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE) 2021-July 522 - 527 2021.07

Research paper (international conference proceedings), Accepted, ISSN 23259000

With the spread of smartphones, the importance of automated testing of mobile applications has increased. However, many current approaches are inadequate, as they are not able to test functions that are available only on hard-to-reach GUI, which is a screen that can be reached only through a specific sequence of input events. To solve this problem, there has been an increase in testing research based on reinforcement learning, specifically Q-learning. Each research uses different learning targets and reward function. Testing research has also been done using Deep Q-Network, which extends reinforcement learning in a “deep” way. Although each work has conducted their own evaluation, it is not clear how the combination of learning algorithm, learning target, and reward function affects the result. To bridge this gap, we have conducted an empirical study comparing eight possible combinations. Our study found that the combination of Deep Q-Network as the learning algorithm, component as the learning target, and GUI change ratio as the reward function had the highest test quality in terms of code coverage.

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バグ発生の制約条件追加によるコンコリックテストのバグ発見能力の向上

武元憲将, 高田眞吾, 名倉正剛

第83回情報処理学会全国大会講演論文集 (情報処理学会) 249 - 250 2021.03

Research paper (conference, symposium, etc.), Joint Work

Coverage-Guided Fairness Testing

Perez Morales D., Kitamura T., Takada S.

Studies in Computational Intelligence (Studies in Computational Intelligence) 985 183 - 199 2021

Software testing is a crucial task. Unlike conventional software, AI software that uses decision-making algorithms or classifiers needs to be tested for discrimination or bias. Such bias can cause discrimination towards certain individuals based on their protected attributes, such as race, gender or nationality. It is a major concern to have discrimination as an unintended behavior. Previous work tested for discrimination randomly, which has resulted in variations in the results for each test execution. These varying results indicate that, for each test execution, there is discrimination that is not found. Even though it is nearly impossible to find all discrimination unless we check all possible combinations in the system, it is important to detect as much discrimination as possible. We thus propose Coverage-Guided Fairness Testing (CGFT). CGFT leverages combinatorial testing to generate an evenly-distributed test suite. We evaluated CGFT with two different datasets, creating three models with each. The results show an improvement in the number of unfairness found using CGFT compared to previous work.

Access to Document (DOI)

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