Pub Date : 2018-07-15DOI: 10.1109/ICSME.2018.00075
R. R. Almeida, U. Kulesza, Christoph Treude, D'angellys Cavalcanti Feitosa, Aliandro Lima
Technical debt (TD) is a metaphor to describe the trade-off between short-term workarounds and long-term goals in software development. Despite being widely used to explain technical issues in business terms, industry and academia still lack a proper way to manage technical debt while explicitly considering business priorities. In this paper, we report on a multiple-case study of how two big software development companies handle technical debt items, and we show how taking the business perspective into account can improve the decision making for the prioritization of technical debt. We also propose a first step toward an approach that uses business process management (BPM) to manage technical debt. We interviewed a set of IT business stakeholders, and we collected and analyzed different sets of technical debt items, comparing how these items would be prioritized using a purely technical versus a business-oriented approach. We found that the use of business process management to support technical debt management makes the technical debt prioritization decision process more aligned with business expectations. We also found evidence that the business process management approach can help technical debt management achieve business objectives.
{"title":"Aligning Technical Debt Prioritization with Business Objectives: A Multiple-Case Study","authors":"R. R. Almeida, U. Kulesza, Christoph Treude, D'angellys Cavalcanti Feitosa, Aliandro Lima","doi":"10.1109/ICSME.2018.00075","DOIUrl":"https://doi.org/10.1109/ICSME.2018.00075","url":null,"abstract":"Technical debt (TD) is a metaphor to describe the trade-off between short-term workarounds and long-term goals in software development. Despite being widely used to explain technical issues in business terms, industry and academia still lack a proper way to manage technical debt while explicitly considering business priorities. In this paper, we report on a multiple-case study of how two big software development companies handle technical debt items, and we show how taking the business perspective into account can improve the decision making for the prioritization of technical debt. We also propose a first step toward an approach that uses business process management (BPM) to manage technical debt. We interviewed a set of IT business stakeholders, and we collected and analyzed different sets of technical debt items, comparing how these items would be prioritized using a purely technical versus a business-oriented approach. We found that the use of business process management to support technical debt management makes the technical debt prioritization decision process more aligned with business expectations. We also found evidence that the business process management approach can help technical debt management achieve business objectives.","PeriodicalId":6572,"journal":{"name":"2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"136 1","pages":"655-664"},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79613975","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 : 2018-07-06DOI: 10.1109/ICSME.2018.00058
A. Sabetta, M. Bezzi
The lack of reliable sources of detailed information on the vulnerabilities of open-source software (OSS) components is a major obstacle to maintaining a secure software supply chain and an effective vulnerability management process. Standard sources of advisories and vulnerability data, such as the National Vulnerability Database (NVD), are known to suffer from poor coverage and inconsistent quality. To reduce our dependency on these sources, we propose an approach that uses machine-learning to analyze source code repositories and to automatically identify commits that are security-relevant (i.e., that are likely to fix a vulnerability). We treat the source code changes introduced by commits as documents written in natural language, classifying them using standard document classification methods. Combining independent classifiers that use information from different facets of commits, our method can yield high precision (80%) while ensuring acceptable recall (43%). In particular, the use of information extracted from the source code changes yields a substantial improvement over the best known approach in state of the art, while requiring a significantly smaller amount of training data and employing a simpler architecture.
{"title":"A Practical Approach to the Automatic Classification of Security-Relevant Commits","authors":"A. Sabetta, M. Bezzi","doi":"10.1109/ICSME.2018.00058","DOIUrl":"https://doi.org/10.1109/ICSME.2018.00058","url":null,"abstract":"The lack of reliable sources of detailed information on the vulnerabilities of open-source software (OSS) components is a major obstacle to maintaining a secure software supply chain and an effective vulnerability management process. Standard sources of advisories and vulnerability data, such as the National Vulnerability Database (NVD), are known to suffer from poor coverage and inconsistent quality. To reduce our dependency on these sources, we propose an approach that uses machine-learning to analyze source code repositories and to automatically identify commits that are security-relevant (i.e., that are likely to fix a vulnerability). We treat the source code changes introduced by commits as documents written in natural language, classifying them using standard document classification methods. Combining independent classifiers that use information from different facets of commits, our method can yield high precision (80%) while ensuring acceptable recall (43%). In particular, the use of information extracted from the source code changes yields a substantial improvement over the best known approach in state of the art, while requiring a significantly smaller amount of training data and employing a simpler architecture.","PeriodicalId":6572,"journal":{"name":"2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"5 1","pages":"579-582"},"PeriodicalIF":0.0,"publicationDate":"2018-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87355591","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 : 2018-06-26DOI: 10.1109/ICSME.2018.00018
Jirayus Jiarpakdee, C. Tantithamthavorn, Christoph Treude
The interpretation of defect models heavily relies on software metrics that are used to construct them. However, such software metrics are often correlated in defect models. Prior work often uses feature selection techniques to remove correlated metrics in order to improve the performance of defect models. Yet, the interpretation of defect models may be misleading if feature selection techniques produce subsets of inconsistent and correlated metrics. In this paper, we investigate the consistency and correlation of the subsets of metrics that are produced by nine commonly-used feature selection techniques. Through a case study of 13 publicly-available defect datasets, we find that feature selection techniques produce inconsistent subsets of metrics and do not mitigate correlated metrics, suggesting that feature selection techniques should not be used and correlation analyses must be applied when the goal is model interpretation. Since correlation analyses often involve manual selection of metrics by a domain expert, we introduce AutoSpearman, an automated metric selection approach based on correlation analyses. Our evaluation indicates that AutoSpearman yields the highest consistency of subsets of metrics among training samples and mitigates correlated metrics, while impacting model performance by 1-2%pts. Thus, to automatically mitigate correlated metrics when interpreting defect models, we recommend future studies use AutoSpearman in lieu of commonly-used feature selection techniques.
{"title":"AutoSpearman: Automatically Mitigating Correlated Software Metrics for Interpreting Defect Models","authors":"Jirayus Jiarpakdee, C. Tantithamthavorn, Christoph Treude","doi":"10.1109/ICSME.2018.00018","DOIUrl":"https://doi.org/10.1109/ICSME.2018.00018","url":null,"abstract":"The interpretation of defect models heavily relies on software metrics that are used to construct them. However, such software metrics are often correlated in defect models. Prior work often uses feature selection techniques to remove correlated metrics in order to improve the performance of defect models. Yet, the interpretation of defect models may be misleading if feature selection techniques produce subsets of inconsistent and correlated metrics. In this paper, we investigate the consistency and correlation of the subsets of metrics that are produced by nine commonly-used feature selection techniques. Through a case study of 13 publicly-available defect datasets, we find that feature selection techniques produce inconsistent subsets of metrics and do not mitigate correlated metrics, suggesting that feature selection techniques should not be used and correlation analyses must be applied when the goal is model interpretation. Since correlation analyses often involve manual selection of metrics by a domain expert, we introduce AutoSpearman, an automated metric selection approach based on correlation analyses. Our evaluation indicates that AutoSpearman yields the highest consistency of subsets of metrics among training samples and mitigates correlated metrics, while impacting model performance by 1-2%pts. Thus, to automatically mitigate correlated metrics when interpreting defect models, we recommend future studies use AutoSpearman in lieu of commonly-used feature selection techniques.","PeriodicalId":6572,"journal":{"name":"2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"33 1","pages":"92-103"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86360826","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 : 2018-06-15DOI: 10.1109/ICSME.2018.00054
Serena Elisa Ponta, H. Plate, A. Sabetta
The use of open-source software (OSS) is ever-increasing, and so is the number of open-source vulnerabilities being discovered and publicly disclosed. The gains obtained from the reuse of community-developed libraries may be offset by the cost of detecting, assessing, and mitigating their vulnerabilities in a timely manner. In this paper we present a novel method to detect, assess and mitigate OSS vulnerabilities that improves on state-of-the-art approaches, which commonly depend on metadata to identify vulnerable OSS dependencies. Our solution instead is code-centric and combines static and dynamic analysis to determine the reachability of the vulnerable portion of libraries used (directly or transitively) by an application. Taking this usage into account, our approach then supports developers in choosing among the existing non-vulnerable library versions. Vulas, the tool implementing our code-centric and usage-based approach, is officially recommended by SAP to scan its Java software, and has been successfully used to perform more than 250000 scans of about 500 applications since December 2016. We report on our experience and on the lessons we learned when maturing the tool from a research prototype to an industrial-grade solution.
{"title":"Beyond Metadata: Code-Centric and Usage-Based Analysis of Known Vulnerabilities in Open-Source Software","authors":"Serena Elisa Ponta, H. Plate, A. Sabetta","doi":"10.1109/ICSME.2018.00054","DOIUrl":"https://doi.org/10.1109/ICSME.2018.00054","url":null,"abstract":"The use of open-source software (OSS) is ever-increasing, and so is the number of open-source vulnerabilities being discovered and publicly disclosed. The gains obtained from the reuse of community-developed libraries may be offset by the cost of detecting, assessing, and mitigating their vulnerabilities in a timely manner. In this paper we present a novel method to detect, assess and mitigate OSS vulnerabilities that improves on state-of-the-art approaches, which commonly depend on metadata to identify vulnerable OSS dependencies. Our solution instead is code-centric and combines static and dynamic analysis to determine the reachability of the vulnerable portion of libraries used (directly or transitively) by an application. Taking this usage into account, our approach then supports developers in choosing among the existing non-vulnerable library versions. Vulas, the tool implementing our code-centric and usage-based approach, is officially recommended by SAP to scan its Java software, and has been successfully used to perform more than 250000 scans of about 500 applications since December 2016. We report on our experience and on the lessons we learned when maturing the tool from a research prototype to an industrial-grade solution.","PeriodicalId":6572,"journal":{"name":"2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"1 1","pages":"449-460"},"PeriodicalIF":0.0,"publicationDate":"2018-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75847261","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 : 2018-06-07DOI: 10.1109/ICSME.2018.00027
Christoph Stanik, Lloyd Montgomery, Daniel Martens, D. Fucci, W. Maalej
Successful open source communities are constantly looking for new members and helping them become active developers. A common approach for developer onboarding in open source projects is to let newcomers focus on relevant yet easy-to-solve issues to familiarize themselves with the code and the community. The goal of this research is twofold. First, we aim at automatically identifying issues that newcomers can resolve by analyzing the history of resolved issues by simply using the title and description of issues. Second, we aim at automatically identifying issues, that can be resolved by newcomers who later become active developers. We mined the issue trackers of three large open source projects and extracted natural language features from the title and description of resolved issues. In a series of experiments, we optimized and compared the accuracy of four supervised classifiers to address our research goals. Random Forest, achieved up to 91% precision (F1-score 72%) towards the first goal while for the second goal, Decision Tree achieved a precision of 92% (F1-score 91%). A qualitative evaluation gave insights on what information in the issue description is helpful for newcomers. Our approach can be used to automatically identify, label, and recommend issues for newcomers in open source software projects based only on the text of the issues.
{"title":"A Simple NLP-Based Approach to Support Onboarding and Retention in Open Source Communities","authors":"Christoph Stanik, Lloyd Montgomery, Daniel Martens, D. Fucci, W. Maalej","doi":"10.1109/ICSME.2018.00027","DOIUrl":"https://doi.org/10.1109/ICSME.2018.00027","url":null,"abstract":"Successful open source communities are constantly looking for new members and helping them become active developers. A common approach for developer onboarding in open source projects is to let newcomers focus on relevant yet easy-to-solve issues to familiarize themselves with the code and the community. The goal of this research is twofold. First, we aim at automatically identifying issues that newcomers can resolve by analyzing the history of resolved issues by simply using the title and description of issues. Second, we aim at automatically identifying issues, that can be resolved by newcomers who later become active developers. We mined the issue trackers of three large open source projects and extracted natural language features from the title and description of resolved issues. In a series of experiments, we optimized and compared the accuracy of four supervised classifiers to address our research goals. Random Forest, achieved up to 91% precision (F1-score 72%) towards the first goal while for the second goal, Decision Tree achieved a precision of 92% (F1-score 91%). A qualitative evaluation gave insights on what information in the issue description is helpful for newcomers. Our approach can be used to automatically identify, label, and recommend issues for newcomers in open source software projects based only on the text of the issues.","PeriodicalId":6572,"journal":{"name":"2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"08 1","pages":"172-182"},"PeriodicalIF":0.0,"publicationDate":"2018-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86132022","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 : 2018-06-05DOI: 10.1109/ICSME.2018.00056
Alexander LeClair, Zachary Eberhart, Collin McMillan
Software Categorization is the task of organizing software into groups that broadly describe the behavior of the software, such as "editors" or "science." Categorization plays an important role in several maintenance tasks, such as repository navigation and feature elicitation. Current approaches attempt to cast the problem as text classification, to make use of the rich body of literature from the NLP domain. However, as we will this paper, algorithms are generally not applicable off-the-shelf to source code; we found that they work well when high-level project descriptions are available, but suffer very large performance penalties when classifying sourcecode and comments only. We propose a set of adaptations to a state-of-the-art neural classification algorithm and perform two evaluations: one with reference data from Debian end-user programs, and one with a set of C/C++ libraries that we hired professional programmers to annotate. We show that our proposed approach achieves performance exceeding that of previous software classification techniques as well as a state-of-the-art neural text classification technique.
{"title":"Adapting Neural Text Classification for Improved Software Categorization","authors":"Alexander LeClair, Zachary Eberhart, Collin McMillan","doi":"10.1109/ICSME.2018.00056","DOIUrl":"https://doi.org/10.1109/ICSME.2018.00056","url":null,"abstract":"Software Categorization is the task of organizing software into groups that broadly describe the behavior of the software, such as \"editors\" or \"science.\" Categorization plays an important role in several maintenance tasks, such as repository navigation and feature elicitation. Current approaches attempt to cast the problem as text classification, to make use of the rich body of literature from the NLP domain. However, as we will this paper, algorithms are generally not applicable off-the-shelf to source code; we found that they work well when high-level project descriptions are available, but suffer very large performance penalties when classifying sourcecode and comments only. We propose a set of adaptations to a state-of-the-art neural classification algorithm and perform two evaluations: one with reference data from Debian end-user programs, and one with a set of C/C++ libraries that we hired professional programmers to annotate. We show that our proposed approach achieves performance exceeding that of previous software classification techniques as well as a state-of-the-art neural text classification technique.","PeriodicalId":6572,"journal":{"name":"2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"25 1","pages":"461-472"},"PeriodicalIF":0.0,"publicationDate":"2018-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74986118","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 : 2018-06-05DOI: 10.1109/ICSME.2018.00050
Alexandre Decan, T. Mens, Eleni Constantinou
Software packages developed and distributed through package managers extensively depend on other packages. These dependencies are regularly updated, for example to add new features, resolve bugs or fix security issues. In order to take full advantage of the benefits of this type of reuse, developers should keep their dependencies up to date by relying on the latest releases. In practice, however, this is not always possible, and packages lag behind with respect to the latest version of their dependencies. This phenomenon is described as technical lag in the literature. In this paper, we perform an empirical study of technical lag in the npm dependency network by investigating its evolution for over 1.4M releases of 120K packages and 8M dependencies between these releases. We explore how technical lag increases over time, taking into account the release type and the use of package dependency constraints. We also discuss how technical lag can be reduced by relying on the semantic versioning policy.
{"title":"On the Evolution of Technical Lag in the npm Package Dependency Network","authors":"Alexandre Decan, T. Mens, Eleni Constantinou","doi":"10.1109/ICSME.2018.00050","DOIUrl":"https://doi.org/10.1109/ICSME.2018.00050","url":null,"abstract":"Software packages developed and distributed through package managers extensively depend on other packages. These dependencies are regularly updated, for example to add new features, resolve bugs or fix security issues. In order to take full advantage of the benefits of this type of reuse, developers should keep their dependencies up to date by relying on the latest releases. In practice, however, this is not always possible, and packages lag behind with respect to the latest version of their dependencies. This phenomenon is described as technical lag in the literature. In this paper, we perform an empirical study of technical lag in the npm dependency network by investigating its evolution for over 1.4M releases of 120K packages and 8M dependencies between these releases. We explore how technical lag increases over time, taking into account the release type and the use of package dependency constraints. We also discuss how technical lag can be reduced by relying on the semantic versioning policy.","PeriodicalId":6572,"journal":{"name":"2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"92 1","pages":"404-414"},"PeriodicalIF":0.0,"publicationDate":"2018-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77010643","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}
Pavneet Singh Kochhar, S. Swierc, Trevor Carnahan, Hitesh Sajnani, M. Nagappan
Work item tracking systems such as Visual Studio Team Services, JIRA, BugZilla and GitHub issue tracker are widely used by software engineers. These systems are used to track work items such as features, user stories, bugs, plan sprints, distribute tasks across the team and prioritize the team's work. Such systems can help teams track the progress and manage the shipping of software. While these tracking systems give data about different work items in tabular format, using a reporting tool on top of them can help teams visualize the data related to their projects such as how many bugs are open and closed and which work items are assigned to a team member. While tools like Visual Studio and JIRA provide reporting services, it is important to understand how users leverage them in their projects to help improve the reporting services. In this study, we conduct an empirical investigation on the usage of Analytics Service - a reporting service provided by Visual Studio Team Services (VSTS) to build dashboards and reports out of their work item tracking data. In particular, we want to understand why and how users interact with Analytics Service and what are the outcomes and business decisions taken by stakeholders from reports built using Analytics Service. We perform semi-structured interviews and survey with users of Analytics Service to understand usage and challenges. Our report on qualitative and quantitative analysis can help organizations and engineers building similar tools or services.
诸如Visual Studio Team Services、JIRA、BugZilla和GitHub问题跟踪器等工作项跟踪系统被软件工程师广泛使用。这些系统用于跟踪工作项，如特性、用户故事、bug、计划冲刺、跨团队分配任务以及确定团队工作的优先级。这样的系统可以帮助团队跟踪进度并管理软件的交付。当这些跟踪系统以表格形式提供关于不同工作项的数据时，在它们上面使用报告工具可以帮助团队可视化与他们的项目相关的数据，例如打开和关闭了多少错误，以及将哪些工作项分配给了团队成员。虽然像Visual Studio和JIRA这样的工具提供了报告服务，但重要的是要了解用户如何在他们的项目中利用它们来帮助改进报告服务。在这项研究中，我们对分析服务的使用进行了实证调查——分析服务是由Visual Studio Team Services (VSTS)提供的一种报告服务，用于根据工作项跟踪数据构建仪表板和报告。特别是，我们想要了解用户为什么以及如何与Analytics Service交互，以及利益相关者从使用Analytics Service构建的报告中获得的结果和业务决策是什么。我们对Analytics Service的用户进行半结构化访谈和调查，以了解使用情况和面临的挑战。我们关于定性和定量分析的报告可以帮助组织和工程师构建类似的工具或服务。
{"title":"Understanding the Role of Reporting in Work Item Tracking Systems for Software Development: An Industrial Case Study","authors":"Pavneet Singh Kochhar, S. Swierc, Trevor Carnahan, Hitesh Sajnani, M. Nagappan","doi":"10.1145/3183440.3195071","DOIUrl":"https://doi.org/10.1145/3183440.3195071","url":null,"abstract":"Work item tracking systems such as Visual Studio Team Services, JIRA, BugZilla and GitHub issue tracker are widely used by software engineers. These systems are used to track work items such as features, user stories, bugs, plan sprints, distribute tasks across the team and prioritize the team's work. Such systems can help teams track the progress and manage the shipping of software. While these tracking systems give data about different work items in tabular format, using a reporting tool on top of them can help teams visualize the data related to their projects such as how many bugs are open and closed and which work items are assigned to a team member. While tools like Visual Studio and JIRA provide reporting services, it is important to understand how users leverage them in their projects to help improve the reporting services. In this study, we conduct an empirical investigation on the usage of Analytics Service - a reporting service provided by Visual Studio Team Services (VSTS) to build dashboards and reports out of their work item tracking data. In particular, we want to understand why and how users interact with Analytics Service and what are the outcomes and business decisions taken by stakeholders from reports built using Analytics Service. We perform semi-structured interviews and survey with users of Analytics Service to understand usage and challenges. Our report on qualitative and quantitative analysis can help organizations and engineers building similar tools or services.","PeriodicalId":6572,"journal":{"name":"2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"68 1","pages":"605-614"},"PeriodicalIF":0.0,"publicationDate":"2018-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84100387","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 : 2018-03-15DOI: 10.1109/ICSME.2018.00089
D. D. Nucci
Software testing is essential for any software development process, representing an extremely expensive activity. Despite its importance recent studies showed that developers rarely test their application and most programming sessions end without any test execution. Indeed, new methods and tools able to better allocating the developers effort are needed to increment the system reliability and to reduce the testing costs. In this work we focus on three activities able to optimize testing activities, specifically, bug prediction, test case prioritization, and energy leaks detection. Indeed, despite the effort devoted in the last decades by the research community led to interesting results, we highlight some aspects that might be improved and propose empirical investigations and novel approaches. Finally, we provide a set of open issues that should be addressed by the research community in the future.
{"title":"Methods and Tools for Focusing and Prioritizing the Testing Effort","authors":"D. D. Nucci","doi":"10.1109/ICSME.2018.00089","DOIUrl":"https://doi.org/10.1109/ICSME.2018.00089","url":null,"abstract":"Software testing is essential for any software development process, representing an extremely expensive activity. Despite its importance recent studies showed that developers rarely test their application and most programming sessions end without any test execution. Indeed, new methods and tools able to better allocating the developers effort are needed to increment the system reliability and to reduce the testing costs. In this work we focus on three activities able to optimize testing activities, specifically, bug prediction, test case prioritization, and energy leaks detection. Indeed, despite the effort devoted in the last decades by the research community led to interesting results, we highlight some aspects that might be improved and propose empirical investigations and novel approaches. Finally, we provide a set of open issues that should be addressed by the research community in the future.","PeriodicalId":6572,"journal":{"name":"2018 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":"23 1","pages":"722-726"},"PeriodicalIF":0.0,"publicationDate":"2018-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73452784","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}
A. Nguyen, Peter C. Rigby, THANH VAN NGUYEN, Dharani Palani, Mark Karanfil, T. Nguyen
We develop T2API, a context-sensitive, graph-based statistical translation approach that takes as input an English description of a programming task and synthesizes the corresponding API code template for the task. We train T2API to statistically learn the alignments between English and API elements and determine the relevant API elements. The training is done on StackOverflow, a bilingual corpus on which developers discuss programming problems in two types of language: English and programming language. T2API considers both the context of the words in the input query and the context of API elements that often go together in the corpus. The derived API elements with their relevance scores are assembled into an API usage by GraSyn, a novel graph-based API synthesis algorithm that generates a graph representing an API usage from a large code corpus. Importantly, it is capable of generating new API usages from previously seen sub-usages. We curate a test benchmark of 250 real-world StackOverflow posts. Across the benchmark, T2API's synthesized snippets have the correct API elements with a median top-1 precision and recall of 67% and 100%, respectively. Four professional developers and five graduate students judged that 77% of our top synthesized API code templates are useful to solve the problem presented in the StackOverflow posts.
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