Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00088
Natthawute Sae-Lim, Shinpei Hayashi, M. Saeki
A decaying module refers to a module whose quality is getting worse and is likely to become smelly in the future. The concept has been proposed to mitigate the problem that developers cannot track the progression of code smells and prevent them from occurring. To support developers in proactive refactoring process to prevent code smells, a prediction approach has been proposed to detect modules that are likely to become decaying modules in the next milestone. Our prior study has shown that modules that developers will modify as an estimation of developers' context can be used to improve the performance of the prediction model significantly. Nevertheless, it requires the developer who has perfect knowledge of locations of changes to manually specify such information to the system. To this end, in this study, we explore the use of automated impact analysis techniques to estimate the developers' context. Such techniques will enable developers to improve the performance of the decaying module prediction model without the need of perfect knowledge or manual input to the system. Furthermore, we conduct a study on the relationship between the accuracy of an impact analysis technique and its effect on improving decaying module prediction, as well as the future direction that should be explored.
{"title":"Can Automated Impact Analysis Techniques Help Predict Decaying Modules?","authors":"Natthawute Sae-Lim, Shinpei Hayashi, M. Saeki","doi":"10.1109/ICSME.2019.00088","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00088","url":null,"abstract":"A decaying module refers to a module whose quality is getting worse and is likely to become smelly in the future. The concept has been proposed to mitigate the problem that developers cannot track the progression of code smells and prevent them from occurring. To support developers in proactive refactoring process to prevent code smells, a prediction approach has been proposed to detect modules that are likely to become decaying modules in the next milestone. Our prior study has shown that modules that developers will modify as an estimation of developers' context can be used to improve the performance of the prediction model significantly. Nevertheless, it requires the developer who has perfect knowledge of locations of changes to manually specify such information to the system. To this end, in this study, we explore the use of automated impact analysis techniques to estimate the developers' context. Such techniques will enable developers to improve the performance of the decaying module prediction model without the need of perfect knowledge or manual input to the system. Furthermore, we conduct a study on the relationship between the accuracy of an impact analysis technique and its effect on improving decaying module prediction, as well as the future direction that should be explored.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128325441","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00103
Anthony S Peruma
Unknowingly, identifiers in the source code of a software system play a vital role in determining the quality of the system. Ambiguous and confusing identifier names lead developers to not only misunderstand the behavior of the code but also increases comprehension time and thereby causes a loss in productivity. Even though correcting poor names through rename operations is a viable option for solving this problem, renaming itself is an act of rework and is not immune to defect injection. In this study, we aim to understand the motivations that drive developers to name and rename identifiers and the decisions they make in determining the name. Using our results, we propose the development of a linguistic model that determines identifier names based on the behavior of the identifier. As a prerequisite to constructing the model, we conduct multiple studies to determine the features that should feed into the model. In this paper, we discuss findings from our completed studies and justify the continuation of research on this topic through further studies.
{"title":"Towards a Model to Appraise and Suggest Identifier Names","authors":"Anthony S Peruma","doi":"10.1109/ICSME.2019.00103","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00103","url":null,"abstract":"Unknowingly, identifiers in the source code of a software system play a vital role in determining the quality of the system. Ambiguous and confusing identifier names lead developers to not only misunderstand the behavior of the code but also increases comprehension time and thereby causes a loss in productivity. Even though correcting poor names through rename operations is a viable option for solving this problem, renaming itself is an act of rework and is not immune to defect injection. In this study, we aim to understand the motivations that drive developers to name and rename identifiers and the decisions they make in determining the name. Using our results, we propose the development of a linguistic model that determines identifier names based on the behavior of the identifier. As a prerequisite to constructing the model, we conduct multiple studies to determine the features that should feed into the model. In this paper, we discuss findings from our completed studies and justify the continuation of research on this topic through further studies.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128244844","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00074
Masahiro Hata, Masashi Nishimoto, Keiji Nishiyama, Hideyuki Kawabata, T. Hironaka
Programs written in the event-driven style that are typical of mobile and/or Web applications are not easy to read through. For the purpose of reducing the burden put on software developers, we propose a tool for supporting program understanding, named OSAIFU, implemented as a plugin for Android Studio. OSAIFU automatically factorizes source code at hand, i.e., extracts implemented features from source code by analyzing relations among program elements and shows the summary to the user as a list of clickable tag clouds. The user can check how each feature is implemented by clicking a tag cloud and looking at the corresponding part in the source code that is highlighted to make it easy to spot. The results of preliminary case studies show the effectiveness of OSAIFU. OSAIFU is available at https://imozuru.github.io/OSAIFU/.
{"title":"OSAIFU: A Source Code Factorizer on Android Studio","authors":"Masahiro Hata, Masashi Nishimoto, Keiji Nishiyama, Hideyuki Kawabata, T. Hironaka","doi":"10.1109/ICSME.2019.00074","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00074","url":null,"abstract":"Programs written in the event-driven style that are typical of mobile and/or Web applications are not easy to read through. For the purpose of reducing the burden put on software developers, we propose a tool for supporting program understanding, named OSAIFU, implemented as a plugin for Android Studio. OSAIFU automatically factorizes source code at hand, i.e., extracts implemented features from source code by analyzing relations among program elements and shows the summary to the user as a list of clickable tag clouds. The user can check how each feature is implemented by clicking a tag cloud and looking at the corresponding part in the source code that is highlighted to make it easy to spot. The results of preliminary case studies show the effectiveness of OSAIFU. OSAIFU is available at https://imozuru.github.io/OSAIFU/.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127077305","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00035
Bo Zhang, Hongyu Zhang, Junjie Chen, Dan Hao, P. Moscato
Metamorphic relations (MRs) describe the invariant relationships between program inputs and outputs. By checking for violations of MRs, faults in programs can be detected. Identifying MRs manually is a tedious and error-prone task. In this paper, we propose AutoMR, a novel method for systematically inferring and cleansing MRs. AutoMR can discover various types of equality and inequality MRs through a search method (particle swarm optimization). It also employs matrix singular-value decomposition and constraint solving techniques to remove the redundant MRs in the search results. Our experiments on 37 numerical programs from two popular open source packages show that AutoMR can effectively infer a set of accurate and succinct MRs and outperform the state-of-the-art method. Furthermore, we show that the discovered MRs have high fault detection ability in mutation testing and differential testing.
{"title":"Automatic Discovery and Cleansing of Numerical Metamorphic Relations","authors":"Bo Zhang, Hongyu Zhang, Junjie Chen, Dan Hao, P. Moscato","doi":"10.1109/ICSME.2019.00035","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00035","url":null,"abstract":"Metamorphic relations (MRs) describe the invariant relationships between program inputs and outputs. By checking for violations of MRs, faults in programs can be detected. Identifying MRs manually is a tedious and error-prone task. In this paper, we propose AutoMR, a novel method for systematically inferring and cleansing MRs. AutoMR can discover various types of equality and inequality MRs through a search method (particle swarm optimization). It also employs matrix singular-value decomposition and constraint solving techniques to remove the redundant MRs in the search results. Our experiments on 37 numerical programs from two popular open source packages show that AutoMR can effectively infer a set of accurate and succinct MRs and outperform the state-of-the-art method. Furthermore, we show that the discovered MRs have high fault detection ability in mutation testing and differential testing.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129128198","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00059
Thazin Win Win Aung, Huan Huo, Yulei Sui
Traceability links between various software artifacts of a system aid software engineers in system comprehension, verification and change impact analysis. Establishing trace links between software artifacts manually is an error-prone and costly task. Recently, studies in automated traceability link recovery area have received broad attention in the software maintenance community aiming to overcome the challenges of manual trace links maintenance process. In these studies, the trace links results generated by an automated trace recovery approach are presented either in a bland textual matrix format (e.g., tabular format) or two-dimensional graphical formats (e.g. tree view, hierarchical leaf node). Therefore, it is challenging for software engineers to explore the inter-relationships between various artifacts at once (e.g., which test cases and source code files/methods are related to a particular requirement). In this position paper, we propose a hierarchical trace map visualization technique to explore inter-relationships between various artifacts at once naturally and intuitively.
{"title":"Interactive Traceability Links Visualization using Hierarchical Trace Map","authors":"Thazin Win Win Aung, Huan Huo, Yulei Sui","doi":"10.1109/ICSME.2019.00059","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00059","url":null,"abstract":"Traceability links between various software artifacts of a system aid software engineers in system comprehension, verification and change impact analysis. Establishing trace links between software artifacts manually is an error-prone and costly task. Recently, studies in automated traceability link recovery area have received broad attention in the software maintenance community aiming to overcome the challenges of manual trace links maintenance process. In these studies, the trace links results generated by an automated trace recovery approach are presented either in a bland textual matrix format (e.g., tabular format) or two-dimensional graphical formats (e.g. tree view, hierarchical leaf node). Therefore, it is challenging for software engineers to explore the inter-relationships between various artifacts at once (e.g., which test cases and source code files/methods are related to a particular requirement). In this position paper, we propose a hierarchical trace map visualization technique to explore inter-relationships between various artifacts at once naturally and intuitively.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127977845","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00097
Reem S. Alsuhaibani
An empirical study to evaluate the effectiveness of using Markov chains in finding and predicting the grammatical patterns of function identifiers found in source code is presented. The study uses a specialized part-of-speech tagger to annotate function identifiers extracted from 20 C++ open-source systems. A dataset of 93K annotated unique function identifiers is created for analysis. The analysis includes using a first-order Markov chain to model part of speech tag sequences of the identifier names, using a probability transition matrix. The evaluation of the model is via a 10-fold cross validation over the entire set of annotated function identifier names. The preliminary results are promising in terms of applicability and accuracy. The model achieved an accuracy median of 91.53% in predicting the most common part of speech tag on a test set. Future work involves utilizing these results in creating a quality assessment and automatic repairing tool for source code function identifiers.
{"title":"Applying Markov Models to Identify Grammatical Patterns of Function Identifiers","authors":"Reem S. Alsuhaibani","doi":"10.1109/ICSME.2019.00097","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00097","url":null,"abstract":"An empirical study to evaluate the effectiveness of using Markov chains in finding and predicting the grammatical patterns of function identifiers found in source code is presented. The study uses a specialized part-of-speech tagger to annotate function identifiers extracted from 20 C++ open-source systems. A dataset of 93K annotated unique function identifiers is created for analysis. The analysis includes using a first-order Markov chain to model part of speech tag sequences of the identifier names, using a probability transition matrix. The evaluation of the model is via a 10-fold cross validation over the entire set of annotated function identifier names. The preliminary results are promising in terms of applicability and accuracy. The model achieved an accuracy median of 91.53% in predicting the most common part of speech tag on a test set. Future work involves utilizing these results in creating a quality assessment and automatic repairing tool for source code function identifiers.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126784095","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00065
Petru Florin Mihancea, Roger Scott
Copy-paste programming is risky. One danger is that it may introduce hard-to-spot bugs in programs due to incomplete or inconsistent adaptation of the original code in the copy. In this tool demo paper we present an extension of the CodeSonar (R) static analysis tool that detects this kind of problems in C/C++ systems. To tune its accuracy, the tool was equipped with an extensible, rich set of novel filtering heuristics. Using it, we have identified several bugs in multiple open-source projects, errors that were confirmed by the maintainers.
{"title":"CodeSonar (R) Extension for Copy-Paste-(Mis) Adapt Error Detection","authors":"Petru Florin Mihancea, Roger Scott","doi":"10.1109/ICSME.2019.00065","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00065","url":null,"abstract":"Copy-paste programming is risky. One danger is that it may introduce hard-to-spot bugs in programs due to incomplete or inconsistent adaptation of the original code in the copy. In this tool demo paper we present an extension of the CodeSonar (R) static analysis tool that detects this kind of problems in C/C++ systems. To tune its accuracy, the tool was equipped with an extensible, rich set of novel filtering heuristics. Using it, we have identified several bugs in multiple open-source projects, errors that were confirmed by the maintainers.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116146400","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00042
Mohammed Alhamed, Tim Storer
A key task during software maintenance is the refinement and elaboration of emerging software issues, such as feature implementations and bug resolution. It includes the annotation of software tasks with additional information, such as criticality, assignee and estimated cost of resolution. This paper reports on a first study to investigate the feasibility of using crowd workers supplied with limited information about an issue and project to provide comparably accurate estimates using planning poker. The paper describes our adaptation of planning poker to crowdsourcing and our initial trials. The results demonstrate the feasibility and potential efficiency of using crowds to deliver estimates. We also review the additional benefit that asking crowds for an estimate brings, in terms of further elaboration of the details of an issue. Finally, we outline our plans for a more extensive evaluation of planning poker in crowds.
{"title":"Estimating Software Task Effort in Crowds","authors":"Mohammed Alhamed, Tim Storer","doi":"10.1109/ICSME.2019.00042","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00042","url":null,"abstract":"A key task during software maintenance is the refinement and elaboration of emerging software issues, such as feature implementations and bug resolution. It includes the annotation of software tasks with additional information, such as criticality, assignee and estimated cost of resolution. This paper reports on a first study to investigate the feasibility of using crowd workers supplied with limited information about an issue and project to provide comparably accurate estimates using planning poker. The paper describes our adaptation of planning poker to crowdsourcing and our initial trials. The results demonstrate the feasibility and potential efficiency of using crowds to deliver estimates. We also review the additional benefit that asking crowds for an estimate brings, in terms of further elaboration of the details of an issue. Finally, we outline our plans for a more extensive evaluation of planning poker in crowds.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122940775","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00041
Christian D. Newman, M. J. Decker, Reem S. Alsuhaibani, Anthony S Peruma, D. Kaushik, Emily Hill
We present a data set of abbreviations and expansions, derived from a set of five open source systems, for use by the research and development communities.
我们提供了一个缩写和扩展的数据集，这些数据集来自一组五个开源系统，供研究和开发社区使用。
{"title":"An Open Dataset of Abbreviations and Expansions","authors":"Christian D. Newman, M. J. Decker, Reem S. Alsuhaibani, Anthony S Peruma, D. Kaushik, Emily Hill","doi":"10.1109/ICSME.2019.00041","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00041","url":null,"abstract":"We present a data set of abbreviations and expansions, derived from a set of five open source systems, for use by the research and development communities.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116501747","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00054
K. Gallagher, Mark E. Fioravanti, Suzanne Kozaitis
This paper outlines the content and techniques used to teach software maintenance to American university sophomores (second year students) who have had 3 semesters of programming. The course uses an introductory text that is geared to the maturity of the audience. By turning the project of the introductory course into a large software evolution exercise, the major topics of software engineering can still be easily introduced and examined. We present the course organization, evaluation rubrics, and student and instructor experiences from six offerings of the course to demonstrate that treating the project in an introductory course as a software evolution exercise on a large, mature system is a viable alternative to the usual (greenfield) approaches. As an added benefit, meaningful contributions to the open source community can be made.
{"title":"Teaching Software Maintenance","authors":"K. Gallagher, Mark E. Fioravanti, Suzanne Kozaitis","doi":"10.1109/ICSME.2019.00054","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00054","url":null,"abstract":"This paper outlines the content and techniques used to teach software maintenance to American university sophomores (second year students) who have had 3 semesters of programming. The course uses an introductory text that is geared to the maturity of the audience. By turning the project of the introductory course into a large software evolution exercise, the major topics of software engineering can still be easily introduced and examined. We present the course organization, evaluation rubrics, and student and instructor experiences from six offerings of the course to demonstrate that treating the project in an introductory course as a software evolution exercise on a large, mature system is a viable alternative to the usual (greenfield) approaches. As an added benefit, meaningful contributions to the open source community can be made.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133411035","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}
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