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Software Technology Research
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 4
1167-1176

Detecting Android-specific smell co-occurrences based on program static analysis and ensemble learning

Wang Zhenqi
Bian Yixin
Ma Ruonan
Bi Boyu
Wang Jinxin
College of Computer Science & Information Engineering, Harbin Normal University, Harbin 150025, China
DOI: 10.19734/j.issn.1001-3695.2024.09.0331

Abstract

Compared to individual types of code smells, the co-occurrence of code smells causes greater harm to programs. Existing research on Android-specific code smells primarily detects single types of smells, neglecting the negative impact of co-occurring smells on Android applications. To address this gap, this paper proposed a co-occurrence detection method for Android smells, which integrated static program analysis and ensemble learning. This initial study identified the co-occurrence of the no low memory resolver smell and member ignoring method smell. Firstly, this paper developed a co-occurrence detection method for Android smells based on static program analysis, along with an automatic generation method for positive and negative samples, and implemented an ASSD tool based on these methods. The tool provided rich training samples for the subsequent ensemble learning model. Then, this paper introduced a soft-voting ensemble learning model to detect Android smell co-occurrence, addressing the limited generalization capability of individual machine learning models. This model not only integrated traditional machine learning models but also improved deep learning models. Experimental results show that this method outperforms the existing detection methods based on static analysis, with a 26.1 percentage point increase in the F1-score. Additionally, the soft-voting ensemble model based on traditional machine learning outperforms the deep learning-based model, achieving a 6.1 percentage point improvement in the F1-score. This method enables effective detection of Android smell co-occurrence.

Foundation Support

黑龙江省高等教育教学改革研究项目(SJGYB2024407)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.09.0331
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 4
Section: Software Technology Research
Pages: 1167-1176
Serial Number: 1001-3695(2025)04-027-1167-10

Publish History

[2025-04-05] Printed Article

Cite This Article

王祯启, 边奕心, 马偌楠, 等. 融合静态程序分析与集成学习的Android代码异味共存检测 [J]. 计算机应用研究, 2025, 42 (4): 1167-1176. (Wang Zhenqi, Bian Yixin, Ma Ruonan, et al. Detecting Android-specific smell co-occurrences based on program static analysis and ensemble learning [J]. Application Research of Computers, 2025, 42 (4): 1167-1176. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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