論文 - 高田　眞吾

Coverage-Guided Fairness Testing

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

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

　概要を見る

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.

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コーディング規約違反メトリクスに基づきソフトウェア変更に対して不具合混入を予測する手法

名倉正剛, 田口健介, 高田眞吾

情報処理学会論文誌 （情報処理学会）  61 （ 4 ） 895 - 907 2020年04月

研究論文（学術雑誌）, 共著, 査読有り

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フォルトローカライゼーションの精度向上に関する研究

謝佳智, 名倉正剛, 高田眞吾

第82回情報処理学会全国大会 講演論文集 （情報処理学会）     235 - 236 2020年03月

研究論文（研究会，シンポジウム資料等）

CC2020 visualization tool preview and review

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

Proceedings of the 51st ACM Technical Symposium on Computer Science Education (SIGCSE2020) （ACM）     169 - 170 2020年02月

研究論文（国際会議プロシーディングス）, 査読有り,  ISSN  9781450367936

　概要を見る

The CC2020 project was charged with two main objectives, to produce a comprehensive report and a visualization tool to provide global guidance in an evolving computing environment as it affects computing baccalaureate degree programs worldwide. T he report is now in draft form and has undergone four rounds of review. The other goal of the CC2020 project is to develop a set of visualization-based tools that will help users to explore questions they may have concerning curricular guidelines, as well as local computing curricula. A visualization tool has been scoped and a prototype built. This special session will unveil the prototype for the SIGCSE community who will have the opportunity to view and review the tool.

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CPPCD: A Token-Based Approach to Detecting Potential Clones

Hung Y.L., Takada S.

IWSC 2020 - Proceedings of the 2020 IEEE 14th International Workshop on Software Clones （IWSC 2020 - Proceedings of the 2020 IEEE 14th International Workshop on Software Clones）     26 - 32 2020年02月

　概要を見る

© 2020 IEEE. Most state-of-the-art clone detection approaches are aimed at finding clones accurately and/or efficiently. Yet, whether a code fragment is a clone often varies according to different people's perspectives and different clone detection tools. In this paper, we present CPPCD (CP-based Potential Clone Detection), a novel token-based approach to detecting potential clones. It generates CP (clone probability) values and CP distribution graphs for developers to decide if a method is a clone. We have evaluated our approach on large-scale software projects written in Java. Our experiments suggest that the majority of clones have CP values greater than or equal to 0.75 and that CPPCD is an accurate (with respect to Type-1, Type-2, and Type-3 clones), efficient, and scalable approach to detecting potential clones.

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Toward the visual understanding of computing curricula

Takada S., Cuadros-Vargas E., Impagliazzo J., Gordon S., Marshall L., Topi H., van der Veer G., Waguespack L.

Education and Information Technologies （Education and Information Technologies）  25 （ 5 ） 4231 - 4270 2020年

ISSN  13602357

　概要を見る

© 2020, The Author(s). Various computing subdisciplines, such as computer science and software engineering, each have their own curricular guidelines. They can be very difficult to understand and compare for people such as prospective students, industry personnel, and even faculty members. This is compounded by a lack of information surrounding undergraduate computing curricular topics via visual methods. This paper describes two experimental activities where the objective is to explore the possibility of obtaining quantitative data sets necessary for visualization, one based on competencies and the other based on knowledge areas. Both activities were based on surveys. The results from the first activity showed that a consensus interpretation could be obtained for the knowledge, skills, and dispositions implied by the competency descriptions, although not as strongly for dispositions. The second activity resulted in a table of knowledge areas with minimum and maximum weights for six computing subdisciplines. Finally, this paper also shows two examples of how users can explore the various curricular guidelines through visualization.

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PfGA を用いたテストスイート自動生成機構

高橋太郎, 高田眞吾

ソフトウェア工学研究会 研究報告 （情報処理学会）  2019-SE-203   1 - 7 2019年12月

研究論文（研究会，シンポジウム資料等）

機能的類似コード検出手法に対する実開発データを利用した評価に関する一考察

名倉 正剛, 高田 眞吾

第26回ソフトウェア工学の基礎ワークショップ (FOSE2019) 講演集    193 - 194 2019年11月

研究論文（研究会，シンポジウム資料等）

コーディング規約違反メトリクスに基づくソフトウェア変更に対する不具合予測手法の提案

名倉正剛, 田口健介, 高田眞吾

ソフトウェアエンジニアリングシンポジウム2019(SES2019) 2019年08月

研究論文（研究会，シンポジウム資料等）, 共著

Semantic analysis for deep Q-network in android GUI testing

Vuong 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）  2019-July   123 - 128 2019年07月

研究論文（国際会議プロシーディングス）, 共著, 査読有り

　概要を見る

Since the big boom of smartphone and consequently of mobile applications, developers nowadays have many tools to help them create applications easier and faster. However, efficient automated testing tools are still missing, especially for GUI testing. We propose an automated GUI testing tool for Android applications using Deep Q-Network and semantic analysis of the GUI. We identify the semantic meanings of GUI elements and use them as an input to a neural network, which through training, approximates the behavioral model of the application under test. The neural network is trained using the Q-Learning algorithm of Reinforcement Learning. It guides the testing tool to explore more often functionalities that can only be accessed through a specific sequence of actions. The tool does not require access to the source code of the application under test. It obtains higher code coverage and is better at fault detection in comparison to state-of-the-art testing tools.

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A reinforcement learning based approach to automated testing of android applications

Vuong T., Takada S.

A-TEST 2018 - Proceedings of the 9th ACM SIGSOFT International Workshop on Automating TEST Case Design, Selection, and Evaluation, Co-located with FSE 2018 （A-TEST 2018 - Proceedings of the 9th ACM SIGSOFT International Workshop on Automating TEST Case Design, Selection, and Evaluation, Co-located with FSE 2018）     31 - 37 2018年11月

研究論文（国際会議プロシーディングス）, 共著, 査読有り

　概要を見る

In recent years, researchers have actively proposed tools to automate testing for Android applications. Their techniques, however, still encounter major difficulties. First is the difficulty of achieving high code coverage because applications usually have a large number of possible combinations of operations and transitions, which makes testing all possible scenarios time-consuming and ineffective for large systems. Second is the difficulty of achieving a wide range of application functionalities, because some functionalities can only be reached through a specific sequence of events. Therefore they are tested less often in random testing. Facing these problems, we apply a reinforcement learning algorithm called Q-learning to take advantage of both random and model-based testing. A Q-learning agent interacts with the Android application, builds a behavioral model gradually and generates test cases based on the model. The agent explores the application in an optimal way that reveals as much functionalities of the application as possible. The exploration using Q-learning improves code coverage in comparison to random and model-based testing and is able to detect faults in applications under test.

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Image-Based Approach to Determining Regression Test Results of Dynamic Web Applications

Hori A., Takada S., Kurabayashi T., Tanno H.

International Journal of Software Engineering and Knowledge Engineering （International Journal of Software Engineering and Knowledge Engineering）  28 （ 7 ） 1001 - 1025 2018年07月

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  02181940

　概要を見る

Much work has been done on automating regression testing for applications. But most of them focus on test execution. Little work has been done on automatically determining if a test case passes or fails. This decision is often made by comparing the results of executing test cases on a base version of the application and post-modification version of the application. If the two results match, the test case passes, otherwise fails. However, to the best of our knowledge, there is no regression testing method for automatically deciding pass/fail of dynamic Web applications which use JavaScript or CSS. We propose a method that automatically decides if a dynamic Web application passes a regression test case. The basic idea is to obtain a screenshot each time the GUI of the Web application (i.e. Web page) changes its state, and then compare each corresponding screenshot to see if they match. The evaluation results showed that the accuracy rate of our approach is high and our approach can be considered as fast enough for practical use.

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Testing android applications using multi-objective evolutionary algorithms with a stopping criteria

Rohella A., 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）  2018-July   308 - 313 2018年07月

研究論文（国際会議プロシーディングス）, 共著, 査読有り

　概要を見る

© 2018 Universitat zu Koln. All rights reserved. The ever increasing usage of Android devices and apps has created a demand for faster and reliable testing techniques. While the quality of test cases can be summed up based on the amount of code they cover, fault detection in applications is one of the main objectives for testing. We introduce an Android app testing approach which uses multiobjective genetic algorithm with elitism which finds optimal test cases by minimizing their length, maximizes the code coverage and fault detection capability, and minimizes the whole test suite for re-usability. In addition to that, we also incorporate a progress indicator which checks for improvements in test suite quality after subsequent generations and use it as a stopping criterion. The effectiveness of our approach is shown in our evaluation where it is able to perform better than the existing state-of-The-Art tools.

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システム障害対処のためのweb検索支援手法の提案

西澤玄, 名倉正剛, 高田眞吾

情報処理学会第80回全国大会 （情報処理学会）     2J-07 2018年03月

研究論文（研究会，シンポジウム資料等）, 共著

Detecting functionally similar code within the same project

Tajima R., Nagura M., Takada S.

2018 IEEE 12th International Workshop on Software Clones, IWSC 2018 - Proceedings （2018 IEEE 12th International Workshop on Software Clones, IWSC 2018 - Proceedings）  2018-January   51 - 57 2018年03月

研究論文（国際会議プロシーディングス）, 共著, 査読有り

　概要を見る

Multiple developers often take part in a software development project. Although these developers are collaborating towards the development within the same project, each developer creates code on their own. This may lead to duplicate or similar code appearing in different parts of the software. Such code should be removed to improve maintainability. This paper proposes an approach to automatically detect such code, which we shall call functionally similar code. The unit of detection is at the method level, and we focus on input/output and the method structure using program dependence graph. We show the results of applying our approach on open source software.

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既存Androidアプリケーションの実装状況に基づいた実装すべきメソッドの提示手法

薄井駿, 名倉正剛, 高田眞吾

日本ソフトウェア科学会第34回大会会議録 （日本ソフトウェア科学会）     FOSE1-1 2017年09月

研究論文（研究会，シンポジウム資料等）, 共著

並行開発支援のための機能的類似コード検出手法の提案

田島諒, 名倉正剛, 高田眞吾

ソフトウェアエンジニアリングシンポジウム(SES2017)会議録 （情報処理学会）     252 - 259 2017年08月

研究論文（研究会，シンポジウム資料等）, 共著

Random GUI Testing of Android Application Using Behavioral Model

Woramet Muangsiri, Shingo Takada

Proceedings of 29th International Conference on Software Engineering & Knowledge Engineering (SEKE 2017) 2017年07月

研究論文（国際会議プロシーディングス）, 共著, 査読有り

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モバイルアプリレビューの評価項目別自動スコアリングに関する研究

平野 智久，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）  2017-SE-195 （ 3 ） 1 - 8 2017年03月

研究論文（研究会，シンポジウム資料等）, 共著

Investigating Tendencies in Callback Method Implementations in Android Applications

Hayato Usui, Masataka Nagura, Shingo Takada

Proceedings of 8th IEEE International Workshop on Empirical Software Engineering in Practice (IWESEP 2017) （IEEE）     23 - 28 2017年03月

研究論文（国際会議プロシーディングス）, 共著, 査読有り