Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00066
Anton V. Uzunov, Matthew Brennan, Mohan Baruwal Chhetri, Quoc Bao Vo, R. Kowalczyk, John Wondoh
In this paper, we introduce a new meta-model that captures core concepts for constructing software architectures for general-purpose, autonomous middleware frameworks that realize internalized and externalized self-adaptivity at both a system- and meta-level in order to achieve antifragility. The proposed meta-model builds on, specializes, and complements existing multi-agent meta-models in line with a previously published reference model for antifragile systems in the cyber domain.
{"title":"AWaRE2-MM: A Meta-Model for Goal-Driven, Contract-Mediated, Team-Centric Autonomous Middleware Frameworks for Antifragility","authors":"Anton V. Uzunov, Matthew Brennan, Mohan Baruwal Chhetri, Quoc Bao Vo, R. Kowalczyk, John Wondoh","doi":"10.1109/APSEC53868.2021.00066","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00066","url":null,"abstract":"In this paper, we introduce a new meta-model that captures core concepts for constructing software architectures for general-purpose, autonomous middleware frameworks that realize internalized and externalized self-adaptivity at both a system- and meta-level in order to achieve antifragility. The proposed meta-model builds on, specializes, and complements existing multi-agent meta-models in line with a previously published reference model for antifragile systems in the cyber domain.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122469773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00058
Puneet Bhateja
Input-output labelled transition system (IOLTS) is a state-based model that is widely used to describe the functional behaviour of a reactive system. However when the same system is observed asynchronously through a pair of unbounded FIFO queues (or channels), its apparent behaviour is different from its actual behaviour. This is because an execution trace of the system could appear distorted in a multitude of ways. The apparent behaviour is called the asynchronous behaviour of the system. It is well known that the asynchronous behaviour can also be described by an infinite-state IOLTS. This description however proves to be appropriate only as long as the channels are assumed to be reliable. The moment we throw in unreliability assumptions, the asynchronous behaviour becomes probabilistic in nature. The plain IOLTS model is simply not expressive enough to capture this probabilistic behaviour. To this end, we in this paper show how the asynchronous behaviour of a reactive system can be captured by Segala's probabilistic automata (SPA). We further show how the SPA expressing the asynchronous behaviour can serve as a reference model for probabilistic testing of asynchronously communicating systems.
{"title":"Probabilistic testing of asynchronously communicating systems","authors":"Puneet Bhateja","doi":"10.1109/APSEC53868.2021.00058","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00058","url":null,"abstract":"Input-output labelled transition system (IOLTS) is a state-based model that is widely used to describe the functional behaviour of a reactive system. However when the same system is observed asynchronously through a pair of unbounded FIFO queues (or channels), its apparent behaviour is different from its actual behaviour. This is because an execution trace of the system could appear distorted in a multitude of ways. The apparent behaviour is called the asynchronous behaviour of the system. It is well known that the asynchronous behaviour can also be described by an infinite-state IOLTS. This description however proves to be appropriate only as long as the channels are assumed to be reliable. The moment we throw in unreliability assumptions, the asynchronous behaviour becomes probabilistic in nature. The plain IOLTS model is simply not expressive enough to capture this probabilistic behaviour. To this end, we in this paper show how the asynchronous behaviour of a reactive system can be captured by Segala's probabilistic automata (SPA). We further show how the SPA expressing the asynchronous behaviour can serve as a reference model for probabilistic testing of asynchronously communicating systems.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133506420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00015
Yang Yu, Minglai Shao, Hongyan Xu, Ying Sun, Wenjun Wang, Bofei Ma
Event detection is a common research topic in visualization. Existing methods always follow an exploration mode, where machine learning algorithms identify events and then analyze them via a visualization system. The detection process does not integrate the expert's experience. In this paper, we propose a novel framework that organizes the original dataset as an integrated graph that allows for Interactive Event Detection (IED) on the graph. Specifically, we formulate the problem Interactive Event Detection as subgraph detection on the graph under expert's interactions. Further, we define a flexible structure called PGraph to model the dataset and then propose an efficient algorithm that returns a subgraph as an event. Our proposed method supports performing various IED tasks under the expert's interactions. We evaluate the utility of our approach by applying it in two scenarios. One uses a social media dataset to study hot events; the other urban burglary dataset is used to detect consecutive burglary cases. Case studies show that our algorithm could detect more global events considering the expert's experience. By quantitative performance experiments, our method outperforms traditional machine detection approaches, especially in the social media dataset; our method's accuracy is higher than baselines at least 10%.
{"title":"PGraph: A Graph-based Structure for Interactive Event Exploration on Social Media","authors":"Yang Yu, Minglai Shao, Hongyan Xu, Ying Sun, Wenjun Wang, Bofei Ma","doi":"10.1109/APSEC53868.2021.00015","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00015","url":null,"abstract":"Event detection is a common research topic in visualization. Existing methods always follow an exploration mode, where machine learning algorithms identify events and then analyze them via a visualization system. The detection process does not integrate the expert's experience. In this paper, we propose a novel framework that organizes the original dataset as an integrated graph that allows for Interactive Event Detection (IED) on the graph. Specifically, we formulate the problem Interactive Event Detection as subgraph detection on the graph under expert's interactions. Further, we define a flexible structure called PGraph to model the dataset and then propose an efficient algorithm that returns a subgraph as an event. Our proposed method supports performing various IED tasks under the expert's interactions. We evaluate the utility of our approach by applying it in two scenarios. One uses a social media dataset to study hot events; the other urban burglary dataset is used to detect consecutive burglary cases. Case studies show that our algorithm could detect more global events considering the expert's experience. By quantitative performance experiments, our method outperforms traditional machine detection approaches, especially in the social media dataset; our method's accuracy is higher than baselines at least 10%.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134052669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00071
Aki Hongo, Naoya Nitta
UML is useful to model a higher abstraction level concepts of the software in a forward engineering context, but it is still challenging to reverse engineer more complex behavior of realistic object-oriented programs (OOPs) based on such visualization techniques. For example in a sequence diagram, an object appears in quite different ways when it serves as a sender or receiver of some message and as a parameter or return value of another message, and thus compound method invocations such as invocation chains and callbacks cannot be represented directly. In this paper, first, we define a dynamic metrics named alternation complexity that indicates the number of alternations of object roles between sender/receiver and parameter/return value within collaboration. Through experiments with 12 professional programmers, we confirmed that the metrics captures a certain aspect of difficulty in comprehending features. Furthermore, we present a dynamic visualization model to directly represent collaboration where the types of object roles frequently change.
{"title":"Towards a Dynamic Visualization of Complex Reverse-Engineered Object Collaboration","authors":"Aki Hongo, Naoya Nitta","doi":"10.1109/APSEC53868.2021.00071","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00071","url":null,"abstract":"UML is useful to model a higher abstraction level concepts of the software in a forward engineering context, but it is still challenging to reverse engineer more complex behavior of realistic object-oriented programs (OOPs) based on such visualization techniques. For example in a sequence diagram, an object appears in quite different ways when it serves as a sender or receiver of some message and as a parameter or return value of another message, and thus compound method invocations such as invocation chains and callbacks cannot be represented directly. In this paper, first, we define a dynamic metrics named alternation complexity that indicates the number of alternations of object roles between sender/receiver and parameter/return value within collaboration. Through experiments with 12 professional programmers, we confirmed that the metrics captures a certain aspect of difficulty in comprehending features. Furthermore, we present a dynamic visualization model to directly represent collaboration where the types of object roles frequently change.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122172427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00017
Lingjia Li, Jian Cao, Qing Qi
Open source software (OSS) development is a highly collaborative process where individuals, groups and organizations interact to develop, operate and maintain software and related artifacts. The developers' sentiment in this process can have an impact on their working willingness and efficiency. Monitoring sentiment factors can help to improve OSS development and management. However, no method has been proposed to dynamically monitor the sentiment phenomena during the OSS development process. In this paper, an approach to detect Negative Sentiment-related Events (NSE) is proposed. It consists of two steps. The first step is to identify the burst interval of negative comments from open source projects, which corresponds to a NSE. The second step is to annotate this NSE with its event type. To support this approach, the types of NSEs in OSS projects are defined through an empirical study and classifiers are trained to annotate event types automatically. Moreover, conversation disentanglement techniques are employed to make the comments extracted more complete. Finally, the factors that have an influence on NSEs in the OSS project are studied.
{"title":"Monitoring Negative Sentiment-Related Events in Open Source Software Projects","authors":"Lingjia Li, Jian Cao, Qing Qi","doi":"10.1109/APSEC53868.2021.00017","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00017","url":null,"abstract":"Open source software (OSS) development is a highly collaborative process where individuals, groups and organizations interact to develop, operate and maintain software and related artifacts. The developers' sentiment in this process can have an impact on their working willingness and efficiency. Monitoring sentiment factors can help to improve OSS development and management. However, no method has been proposed to dynamically monitor the sentiment phenomena during the OSS development process. In this paper, an approach to detect Negative Sentiment-related Events (NSE) is proposed. It consists of two steps. The first step is to identify the burst interval of negative comments from open source projects, which corresponds to a NSE. The second step is to annotate this NSE with its event type. To support this approach, the types of NSEs in OSS projects are defined through an empirical study and classifiers are trained to annotate event types automatically. Moreover, conversation disentanglement techniques are employed to make the comments extracted more complete. Finally, the factors that have an influence on NSEs in the OSS project are studied.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133898679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00053
M. A. Raj, Jan Bosch, H. H. Olsson, Anders Jansson
The impact of the Artificial Intelligence revolution is undoubtedly substantial in our society, life, firms, and employment. With data being a critical element, organizations are working towards obtaining high-quality data to train their AI models. Although data, data management, and data pipelines are part of industrial practice even before the introduction of ML models, the significance of data increased further with the advent of ML models, which force data pipeline developers to go beyond the traditional focus on data quality. The objective of this study is to analyze the impact of ML use cases on data pipelines. We assume that the data pipelines that serve ML models are given more importance compared to the conventional data pipelines. We report on a study that we conducted by observing software teams at three companies as they develop both conventional(Non-ML) data pipelines and data pipelines that serve ML-based applications. We study six data pipelines from three companies and categorize them based on their criticality and purpose. Further, we identify the determinants that can be used to compare the development and maintenance of these data pipelines. Finally, we map these factors in a two-dimensional space to illustrate their importance on a scale of low, moderate, and high.
{"title":"On the Impact of ML use cases on Industrial Data Pipelines","authors":"M. A. Raj, Jan Bosch, H. H. Olsson, Anders Jansson","doi":"10.1109/APSEC53868.2021.00053","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00053","url":null,"abstract":"The impact of the Artificial Intelligence revolution is undoubtedly substantial in our society, life, firms, and employment. With data being a critical element, organizations are working towards obtaining high-quality data to train their AI models. Although data, data management, and data pipelines are part of industrial practice even before the introduction of ML models, the significance of data increased further with the advent of ML models, which force data pipeline developers to go beyond the traditional focus on data quality. The objective of this study is to analyze the impact of ML use cases on data pipelines. We assume that the data pipelines that serve ML models are given more importance compared to the conventional data pipelines. We report on a study that we conducted by observing software teams at three companies as they develop both conventional(Non-ML) data pipelines and data pipelines that serve ML-based applications. We study six data pipelines from three companies and categorize them based on their criticality and purpose. Further, we identify the determinants that can be used to compare the development and maintenance of these data pipelines. Finally, we map these factors in a two-dimensional space to illustrate their importance on a scale of low, moderate, and high.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116155022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information Retrieval (IR) techniques have been widely utilized by a growing number of software maintenance activities. However, there is a mismatch between source code lexicon (especially identifiers) and vocabulary in software artifacts, leading to the inefficiency of IR techniques. Consequently, it is essential to normalize identifiers, whose aim is to parse identifiers into several natural language terms. Identifier splitting significantly impacts on the effectiveness of identifier normalization. Even though researchers have proposed several approaches to split identifiers, three main drawbacks remain to be resolved, including without considering morphemes, over-splitting, and under-splitting. In this paper, we propose a new Character-level Hybrid-granularity Identifier Splitting approach CHIS to resolve the three drawbacks and better split identifiers. CHIS combines the Bidirectional Encoder Representation from Transformers (BERT) and Conditional Random Fields (CRF) to train a deep learning model to split identifiers. In addition, CHIS further employs a pre-processing component and a post-processing component to resolve the morpheme acquisition drawback and the over-splitting as well as the under-splitting drawbacks respectively, thus further improving its performance. Specifically, in the pre-processing component, CHIS obtains and labels the most frequent subwords of the training identifiers as morphemes through the Byte Pair Encoding (BPE) algorithm and the sequence labeling algorithm. In the post-processing component, CHIS iteratively merges and splits the splitting results obtained by the deep learning model to resolve the over-splitting and under-splitting drawbacks. We conduct extensive experiments to show the effectiveness of CHIS. Experimental results show that CHIS achieves the Accuracy of 0.943 on average and outperforms the state-of-the-art approach by 0.085 on average. In addition, the effectiveness of the pre-processing and post-processing components of CHIS are also validated.
{"title":"CHIS: A Novel Hybrid Granularity Identifier Splitting Approach","authors":"Siyuan Liu, Jingxuan Zhang, Jiahui Liang, Junpeng Luo, Yong Xu, Chenxing Sun","doi":"10.1109/APSEC53868.2021.00027","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00027","url":null,"abstract":"Information Retrieval (IR) techniques have been widely utilized by a growing number of software maintenance activities. However, there is a mismatch between source code lexicon (especially identifiers) and vocabulary in software artifacts, leading to the inefficiency of IR techniques. Consequently, it is essential to normalize identifiers, whose aim is to parse identifiers into several natural language terms. Identifier splitting significantly impacts on the effectiveness of identifier normalization. Even though researchers have proposed several approaches to split identifiers, three main drawbacks remain to be resolved, including without considering morphemes, over-splitting, and under-splitting. In this paper, we propose a new Character-level Hybrid-granularity Identifier Splitting approach CHIS to resolve the three drawbacks and better split identifiers. CHIS combines the Bidirectional Encoder Representation from Transformers (BERT) and Conditional Random Fields (CRF) to train a deep learning model to split identifiers. In addition, CHIS further employs a pre-processing component and a post-processing component to resolve the morpheme acquisition drawback and the over-splitting as well as the under-splitting drawbacks respectively, thus further improving its performance. Specifically, in the pre-processing component, CHIS obtains and labels the most frequent subwords of the training identifiers as morphemes through the Byte Pair Encoding (BPE) algorithm and the sequence labeling algorithm. In the post-processing component, CHIS iteratively merges and splits the splitting results obtained by the deep learning model to resolve the over-splitting and under-splitting drawbacks. We conduct extensive experiments to show the effectiveness of CHIS. Experimental results show that CHIS achieves the Accuracy of 0.943 on average and outperforms the state-of-the-art approach by 0.085 on average. In addition, the effectiveness of the pre-processing and post-processing components of CHIS are also validated.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"292 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124197305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00068
Scott Lupton, H. Washizaki, Nobukazu Yoshioka, Y. Fukazawa
The use of anomaly detection for log monitoring requires parsing model input features from raw, unstructured data. Log parsing methods come in many forms, but are generally categorized as being either offline or online. In this study, a systematic literature review of anomaly detection approaches utilizing online parsing methods is performed. An inventory of these approaches is taken, research gaps are explored, and suggestions for future exploration and study are presented.
{"title":"Literature Review on Log Anomaly Detection Approaches Utilizing Online Parsing Methodology*","authors":"Scott Lupton, H. Washizaki, Nobukazu Yoshioka, Y. Fukazawa","doi":"10.1109/APSEC53868.2021.00068","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00068","url":null,"abstract":"The use of anomaly detection for log monitoring requires parsing model input features from raw, unstructured data. Log parsing methods come in many forms, but are generally categorized as being either offline or online. In this study, a systematic literature review of anomaly detection approaches utilizing online parsing methods is performed. An inventory of these approaches is taken, research gaps are explored, and suggestions for future exploration and study are presented.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121480127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Although researchers have created many advanced code-clone detection techniques, more effort is required to realize wide adaptation of these techniques in the industry. One of the reasons behind this is the reliance of these advanced techniques on lexing and parsing programs. Modern programming languages have complex lexical conventions and grammar, which evolve constantly. Therefore, using advanced code-clone detection techniques requires substantial and continuous effort. This paper proposes a lightweight language-independent method to detect code clones by simply finding repeated strings in a code repository, relying on neither lexing nor parsing. The proposed method is based on an efficient technique developed in a bio-informatics context to find repeated strings. We refer to the repeated strings in the source-code as weak Type-1 clones. Because the proposed technique normalizes newlines, tabs, and white spaces into a single white space, it can find clones in which newline positions or indentations are changed, as often in the case when copy-pasting occurs. Although the proposed method only finds verbatim copies, it also makes interesting observations regarding repository structures. Many developers may prefer the proposed simple approach because it is easier to understand than other advanced techniques that use heuristics, approximation, and machine learning.
{"title":"Finding repeated strings in code repositories and its applications to code-clone detection","authors":"Yoriyuki Yamagata, Fabien Hervé, Yuji Fujiwara, Katsuro Inoue","doi":"10.1109/APSEC53868.2021.00057","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00057","url":null,"abstract":"Although researchers have created many advanced code-clone detection techniques, more effort is required to realize wide adaptation of these techniques in the industry. One of the reasons behind this is the reliance of these advanced techniques on lexing and parsing programs. Modern programming languages have complex lexical conventions and grammar, which evolve constantly. Therefore, using advanced code-clone detection techniques requires substantial and continuous effort. This paper proposes a lightweight language-independent method to detect code clones by simply finding repeated strings in a code repository, relying on neither lexing nor parsing. The proposed method is based on an efficient technique developed in a bio-informatics context to find repeated strings. We refer to the repeated strings in the source-code as weak Type-1 clones. Because the proposed technique normalizes newlines, tabs, and white spaces into a single white space, it can find clones in which newline positions or indentations are changed, as often in the case when copy-pasting occurs. Although the proposed method only finds verbatim copies, it also makes interesting observations regarding repository structures. Many developers may prefer the proposed simple approach because it is easier to understand than other advanced techniques that use heuristics, approximation, and machine learning.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129857211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/APSEC53868.2021.00043
Yusuke Shinyama, Yoshitaka Arahori, K. Gondow
We investigated how programmers express high-level concepts such as path names and coordinates using primitive data types. While relying too much on primitive data types is sometimes criticized as a bad smell, it is still a common practice among programmers. We propose a novel way to accurately identify expressions for certain predefined concepts by examining API calls. We defined twelve conceptual types used in the Java Standard API. We then obtained expressions for each conceptual type from 26 open source projects. Based on the expressions obtained, we trained a decision tree-based classifier. It achieved 83 % F -score for correctly predicting the conceptual type for a given expression. Our result indicates that it is possible to infer a conceptual type from a source code reasonably well once enough examples are given. The obtained classifier can be used for potential bug detection, test case generation and documentation.
我们研究了程序员如何使用基本数据类型表达高级概念，如路径名和坐标。虽然过分依赖原始数据类型有时被批评为一种不好的气味，但这仍然是程序员的常见做法。我们提出了一种新的方法，通过检查API调用来准确地识别某些预定义概念的表达式。我们定义了Java Standard API中使用的12种概念类型。然后，我们从26个开源项目中获得了每个概念类型的表达式。基于得到的表达式，我们训练了一个基于决策树的分类器。它在正确预测给定表达式的概念类型方面获得了83%的F分。我们的结果表明，只要给出足够多的例子，就有可能从源代码中很好地推断出概念类型。获得的分类器可用于潜在的错误检测、测试用例生成和文档。
{"title":"How Do Programmers Express High-Level Concepts using Primitive Data Types?","authors":"Yusuke Shinyama, Yoshitaka Arahori, K. Gondow","doi":"10.1109/APSEC53868.2021.00043","DOIUrl":"https://doi.org/10.1109/APSEC53868.2021.00043","url":null,"abstract":"We investigated how programmers express high-level concepts such as path names and coordinates using primitive data types. While relying too much on primitive data types is sometimes criticized as a bad smell, it is still a common practice among programmers. We propose a novel way to accurately identify expressions for certain predefined concepts by examining API calls. We defined twelve conceptual types used in the Java Standard API. We then obtained expressions for each conceptual type from 26 open source projects. Based on the expressions obtained, we trained a decision tree-based classifier. It achieved 83 % F -score for correctly predicting the conceptual type for a given expression. Our result indicates that it is possible to infer a conceptual type from a source code reasonably well once enough examples are given. The obtained classifier can be used for potential bug detection, test case generation and documentation.","PeriodicalId":143800,"journal":{"name":"2021 28th Asia-Pacific Software Engineering Conference (APSEC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126687066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：