Tarek M. Ahmed, C. Bezemer, T. Chen, A. Hassan, Weiyi Shang
Performance regressions, such as a higher CPU utilization than in the previous version of an application, are caused by software application updates that negatively affect the performance of an application.Although a plethora of mining software repository research has been done to detect such regressions, research tools are generally not readily available to practitioners. Application Performance Management (APM) tools are commonly used in practice for detecting performance issues in the field by mining operational data.In contrast to performance regression detection tools that assume a changing code base and a stable workload, APM tools mine operational data to detect performance anomalies caused by a changing workload in an otherwise stable code base.Although APM tools are widely used in practice, no research has been done to understand 1) whether APM tools can identify performance regressions caused by code changes and 2) how well these APM tools support diagnosing the root-cause of these regressions.In this paper, we explore if the readily accessible APM tools can help practitioners detect performance regressions. We perform a case study using three commercial (AppDynamics, New Relic and Dynatrace) and one open source (Pinpoint) APM tools. In particular, we examine the effectiveness of leveraging these APM tools in detecting and diagnosing injected performance regressions (excessive memory usage, high CPU utilization and inefficient database queries) in three open source applications. We find that APM tools can detect most of the injected performance regressions, making them good candidates to detect performance regressions in practice. However, there is a gap between mining approaches that are proposed in state-of-the-art performance regression detection research and the ones used by APM tools. In addition, APM tools lack the ability to be extended, which makes it hard to enhance them when exploring novel mining approaches for detecting performance regressions.
由于软件应用程序更新会对应用程序的性能产生负面影响，导致性能下降，例如比以前版本的应用程序的CPU利用率更高。尽管已经做了大量的挖掘软件存储库研究来检测这种回归，但研究工具通常不容易为从业者所用。应用程序性能管理(APM)工具在实践中通常用于通过挖掘操作数据来检测现场的性能问题。与假设不断变化的代码库和稳定的工作负载的性能回归检测工具不同，APM工具挖掘操作数据来检测由稳定的代码库中不断变化的工作负载引起的性能异常。尽管APM工具在实践中被广泛使用，但还没有研究来了解1)APM工具是否可以识别由代码更改引起的性能退化，以及2)这些APM工具在诊断这些退化的根本原因方面有多好。在本文中，我们将探讨易于访问的APM工具是否可以帮助从业者检测性能回归。我们使用三个商业APM工具(AppDynamics, New Relic和Dynatrace)和一个开源APM工具(Pinpoint)进行案例研究。我们特别研究了在三个开源应用程序中利用这些APM工具检测和诊断注入的性能退化(内存使用过多、CPU利用率高和数据库查询效率低下)的有效性。我们发现APM工具可以检测到大多数注入的性能回归，使其成为实践中检测性能回归的良好候选者。然而，在最先进的性能回归检测研究中提出的挖掘方法与APM工具使用的方法之间存在差距。此外，APM工具缺乏扩展能力，这使得在探索用于检测性能回归的新挖掘方法时难以对其进行增强。
{"title":"Studying the Effectiveness of Application Performance Management (APM) Tools for Detecting Performance Regressions for Web Applications: An Experience Report","authors":"Tarek M. Ahmed, C. Bezemer, T. Chen, A. Hassan, Weiyi Shang","doi":"10.1145/2901739.2901774","DOIUrl":"https://doi.org/10.1145/2901739.2901774","url":null,"abstract":"Performance regressions, such as a higher CPU utilization than in the previous version of an application, are caused by software application updates that negatively affect the performance of an application.Although a plethora of mining software repository research has been done to detect such regressions, research tools are generally not readily available to practitioners. Application Performance Management (APM) tools are commonly used in practice for detecting performance issues in the field by mining operational data.In contrast to performance regression detection tools that assume a changing code base and a stable workload, APM tools mine operational data to detect performance anomalies caused by a changing workload in an otherwise stable code base.Although APM tools are widely used in practice, no research has been done to understand 1) whether APM tools can identify performance regressions caused by code changes and 2) how well these APM tools support diagnosing the root-cause of these regressions.In this paper, we explore if the readily accessible APM tools can help practitioners detect performance regressions. We perform a case study using three commercial (AppDynamics, New Relic and Dynatrace) and one open source (Pinpoint) APM tools. In particular, we examine the effectiveness of leveraging these APM tools in detecting and diagnosing injected performance regressions (excessive memory usage, high CPU utilization and inefficient database queries) in three open source applications. We find that APM tools can detect most of the injected performance regressions, making them good candidates to detect performance regressions in practice. However, there is a gap between mining approaches that are proposed in state-of-the-art performance regression detection research and the ones used by APM tools. In addition, APM tools lack the ability to be extended, which makes it hard to enhance them when exploring novel mining approaches for detecting performance regressions.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"PC-24 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84847527","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}
The study of the evolution of highly configurable systems requires a thorough understanding of thee core ingredients of such systems: (1) the underlying variability model; (2) the assets that together implement the configurable features; and (3) the mapping from variable features to actual assets. Unfortunately, to date no systematic way to obtain such information at a sufficiently fine grained level exists.To remedy this problem we propose FEVER and its instantiation for the Linux kernel. FEVER extracts detailed information on changes in variability models (KConfig files), assets (preprocessor based C code), and mappings (Make- files). We describe how FEVER works, and apply it to several releases of the Linux kernel. Our evaluation on 300 ran- domly selected commits, from two different releases, shows our results are accurate in 82.6% of the commits. Furthermore, we illustrate how the populated FEVER graph database thus obtained can be used in typical Linux engineering tasks.
{"title":"FEVER: Extracting Feature-oriented Changes from Commits","authors":"Nicolas Dintzner, A. Deursen, M. Pinzger","doi":"10.1145/2901739.2901755","DOIUrl":"https://doi.org/10.1145/2901739.2901755","url":null,"abstract":"The study of the evolution of highly configurable systems requires a thorough understanding of thee core ingredients of such systems: (1) the underlying variability model; (2) the assets that together implement the configurable features; and (3) the mapping from variable features to actual assets. Unfortunately, to date no systematic way to obtain such information at a sufficiently fine grained level exists.To remedy this problem we propose FEVER and its instantiation for the Linux kernel. FEVER extracts detailed information on changes in variability models (KConfig files), assets (preprocessor based C code), and mappings (Make- files). We describe how FEVER works, and apply it to several releases of the Linux kernel. Our evaluation on 300 ran- domly selected commits, from two different releases, shows our results are accurate in 82.6% of the commits. Furthermore, we illustrate how the populated FEVER graph database thus obtained can be used in typical Linux engineering tasks.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"20 1","pages":"85-96"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83937991","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}
Software development remains a predominantly male activity, despite coordinated efforts from research, industry, and policy makers. This gender imbalance is most visible in social programming, on platforms such as Stack Overflow.To better understand the reasons behind this disparity, and off er support for (corrective) decision making, we and others have been engaged in large-scale empirical studies of activity in these online platforms, in which gender is one of the variables of interest. However, since gender is not explicitly recorded, it is typically inferred by automatic "gender guessers", based on cues derived from an individual's online presence, such as their name and profi le picture. As opposed to self-reporting, used in earlier studies, gender guessers scale better, but their accuracy depends on the quantity and quality of data available in one's online pro le.In this paper we evaluate the applicability of different gender guessing approaches on several datasets derived from Stack Overflow. Our results suggest that the approaches combining different data sources perform the best.
{"title":"Recognizing Gender of Stack Overflow Users","authors":"B. Lin, Alexander Serebrenik","doi":"10.1145/2901739.2901777","DOIUrl":"https://doi.org/10.1145/2901739.2901777","url":null,"abstract":"Software development remains a predominantly male activity, despite coordinated efforts from research, industry, and policy makers. This gender imbalance is most visible in social programming, on platforms such as Stack Overflow.To better understand the reasons behind this disparity, and off er support for (corrective) decision making, we and others have been engaged in large-scale empirical studies of activity in these online platforms, in which gender is one of the variables of interest. However, since gender is not explicitly recorded, it is typically inferred by automatic \"gender guessers\", based on cues derived from an individual's online presence, such as their name and profi le picture. As opposed to self-reporting, used in earlier studies, gender guessers scale better, but their accuracy depends on the quantity and quality of data available in one's online pro le.In this paper we evaluate the applicability of different gender guessing approaches on several datasets derived from Stack Overflow. Our results suggest that the approaches combining different data sources perform the best.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"42 1","pages":"425-429"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77614552","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}
M. Asaduzzaman, Md Ahasanuzzaman, C. Roy, Kevin A. Schneider
Exception handling is a technique that addresses exceptional conditions in applications, allowing the normal flow of execution to continue in the event of an exception and/or to report on such events. Although exception handling techniques, features and bad coding practices have been discussed both in developer communities and in the literature, there is a marked lack of empirical evidence on how developers use exception handling in practice. In this paper we use the Boa language and infrastructure to analyze 274k open source Java projects in GitHub to discover how developers use exception handling. We not only consider various exception handling features but also explore bad coding practices and their relation to the experience of developers. Our results provide some interesting insights. For example, we found that bad exception handling coding practices are common in open source Java projects and regardless of experience all developers use bad exception handling coding practices.
{"title":"How Developers Use Exception Handling in Java?","authors":"M. Asaduzzaman, Md Ahasanuzzaman, C. Roy, Kevin A. Schneider","doi":"10.1145/2901739.2903500","DOIUrl":"https://doi.org/10.1145/2901739.2903500","url":null,"abstract":"Exception handling is a technique that addresses exceptional conditions in applications, allowing the normal flow of execution to continue in the event of an exception and/or to report on such events. Although exception handling techniques, features and bad coding practices have been discussed both in developer communities and in the literature, there is a marked lack of empirical evidence on how developers use exception handling in practice. In this paper we use the Boa language and infrastructure to analyze 274k open source Java projects in GitHub to discover how developers use exception handling. We not only consider various exception handling features but also explore bad coding practices and their relation to the experience of developers. Our results provide some interesting insights. For example, we found that bad exception handling coding practices are common in open source Java projects and regardless of experience all developers use bad exception handling coding practices.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"45 1","pages":"516-519"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81333219","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}
Technical debt is a metaphor introduced by Cunningham to indicate "not quite right code which we postpone making it right". Examples of technical debt are code smells and bug hazards. Several techniques have been proposed to detect different types of technical debt. Among those, Potdar and Shihab defined heuristics to detect instances of self-admitted technical debt in code comments, and used them to perform an empirical study on five software systems to investigate the phenomenon. Still, very little is known about the diffusion and evolution of technical debt in software projects.This paper presents a differentiated replication of the work by Potdar and Shihab. We run a study across 159 software projects to investigate the diffusion and evolution of self-admitted technical debt and its relationship with software quality. The study required the mining of over 600K commits and 2 Billion comments as well as a qualitative analysis performed via open coding.Our main findings show that self-admitted technical debt (i) is diffused, with an average of 51 instances per system, (ii) is mostly represented by code (30%), defect, and requirement debt (20% each), (iii) increases over time due to the introduction of new instances that are not fixed by developers, and (iv) even when fixed, it survives long time (over 1,000 commits on average) in the system.
{"title":"A Large-Scale Empirical Study on Self-Admitted Technical Debt","authors":"G. Bavota, B. Russo","doi":"10.1145/2901739.2901742","DOIUrl":"https://doi.org/10.1145/2901739.2901742","url":null,"abstract":"Technical debt is a metaphor introduced by Cunningham to indicate \"not quite right code which we postpone making it right\". Examples of technical debt are code smells and bug hazards. Several techniques have been proposed to detect different types of technical debt. Among those, Potdar and Shihab defined heuristics to detect instances of self-admitted technical debt in code comments, and used them to perform an empirical study on five software systems to investigate the phenomenon. Still, very little is known about the diffusion and evolution of technical debt in software projects.This paper presents a differentiated replication of the work by Potdar and Shihab. We run a study across 159 software projects to investigate the diffusion and evolution of self-admitted technical debt and its relationship with software quality. The study required the mining of over 600K commits and 2 Billion comments as well as a qualitative analysis performed via open coding.Our main findings show that self-admitted technical debt (i) is diffused, with an average of 51 instances per system, (ii) is mostly represented by code (30%), defect, and requirement debt (20% each), (iii) increases over time due to the introduction of new instances that are not fixed by developers, and (iv) even when fixed, it survives long time (over 1,000 commits on average) in the system.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"27 1","pages":"315-326"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77020859","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}
Studying software repositories and hosting services can provide valuable insights into the behaviors of large groups of software developers and their projects. Traditionally, most analysis of metadata collected from hosting services has been conducted by specifying some short window of time, typically just a few years. To date, few - if any - studies have been built from data comprising the entirety of a repository's lifespan: from its birth to its death, and rebirth. Thus, the first contribution of this data set is to support the historical analysis of over ten years of collected metadata from the now-defunct RubyForge project hosting site, as well as the follow-on successor to RubyForge, the RubyGems hosting facility. The data sets and sample analyses in this paper will be relevant to researchers studying both software evolution and the distributed software development process.
{"title":"Data Sets: The Circle of Life in Ruby Hosting, 2003-2015","authors":"Megan Squire","doi":"10.1145/2901739.2903509","DOIUrl":"https://doi.org/10.1145/2901739.2903509","url":null,"abstract":"Studying software repositories and hosting services can provide valuable insights into the behaviors of large groups of software developers and their projects. Traditionally, most analysis of metadata collected from hosting services has been conducted by specifying some short window of time, typically just a few years. To date, few - if any - studies have been built from data comprising the entirety of a repository's lifespan: from its birth to its death, and rebirth. Thus, the first contribution of this data set is to support the historical analysis of over ten years of collected metadata from the now-defunct RubyForge project hosting site, as well as the follow-on successor to RubyForge, the RubyGems hosting facility. The data sets and sample analyses in this paper will be relevant to researchers studying both software evolution and the distributed software development process.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"45 1","pages":"452-459"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78697405","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}
Waqar Ahmad, Christian Kästner, Joshua Sunshine, Jonathan Aldrich
The Android platform is designed to support mutually un-trusted third-party apps, which run as isolated processes but may interact via platform-controlled mechanisms, called Intents. Interactions among third-party apps are intended and can contribute to a rich user experience, for example, the ability to share pictures from one app with another. The Android platform presents an interesting point in a design space of module systems that is biased toward isolation, extensibility, and untrusted contributions. The Intent mech- anism essentially provides message channels among modules, in which the set of message types is extensible. However, the module system has design limitations including the lack of consistent mechanisms to document message types, very limited checking that a message conforms to its specifica- tions, the inability to explicitly declare dependencies on other modules, and the lack of checks for backward compatibility as message types evolve over time. In order to understand the degree to which these design limitations result in real issues, we studied a broad corpus of apps and cross-validated our results against app documentation and Android support forums. Our findings suggest that design limitations do in- deed cause development problems. Based on our results, we outline further research questions and propose possible mitigation strategies.
{"title":"Inter-app Communication in Android: Developer Challenges","authors":"Waqar Ahmad, Christian Kästner, Joshua Sunshine, Jonathan Aldrich","doi":"10.1145/2901739.2901762","DOIUrl":"https://doi.org/10.1145/2901739.2901762","url":null,"abstract":"The Android platform is designed to support mutually un-trusted third-party apps, which run as isolated processes but may interact via platform-controlled mechanisms, called Intents. Interactions among third-party apps are intended and can contribute to a rich user experience, for example, the ability to share pictures from one app with another. The Android platform presents an interesting point in a design space of module systems that is biased toward isolation, extensibility, and untrusted contributions. The Intent mech- anism essentially provides message channels among modules, in which the set of message types is extensible. However, the module system has design limitations including the lack of consistent mechanisms to document message types, very limited checking that a message conforms to its specifica- tions, the inability to explicitly declare dependencies on other modules, and the lack of checks for backward compatibility as message types evolve over time. In order to understand the degree to which these design limitations result in real issues, we studied a broad corpus of apps and cross-validated our results against app documentation and Android support forums. Our findings suggest that design limitations do in- deed cause development problems. Based on our results, we outline further research questions and propose possible mitigation strategies.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"58 1","pages":"177-188"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74951211","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}
Software energy consumption is a relatively new concern for mobile application developers. Poor energy performance can harm adoption and sales of applications. Unfortunately for the developers, the measurement of software energy con-sumption is expensive in terms of hardware and difficult in terms of expertise. Many prior models of software energy consumption assume that developers can use hardware instrumentation and thus cannot evaluate software runningwithin emulators or virtual machines. Some prior modelsrequire actual energy measurements from the previous versions of applications in order to model the energy consumption of later versions of the same application.In this paper, we take a big-data approach to software energy consumption and present a model that can estimate software energy consumption mostly within 10% error (in joules) and does not require the developer to train on energy measurements of their own applications. This model leverages a big-data approach whereby a collection of prior applications’ energy measurements allows us to train, trans-mit, and apply the model to estimate any foreign application’s energy consumption for a test run. Our model is based on the dynamic traces of system calls and CPU utilization.
{"title":"GreenOracle: Estimating Software Energy Consumption with Energy Measurement Corpora","authors":"S. Chowdhury, Abram Hindle","doi":"10.1145/2901739.2901763","DOIUrl":"https://doi.org/10.1145/2901739.2901763","url":null,"abstract":"Software energy consumption is a relatively new concern for mobile application developers. Poor energy performance can harm adoption and sales of applications. Unfortunately for the developers, the measurement of software energy con-sumption is expensive in terms of hardware and difficult in terms of expertise. Many prior models of software energy consumption assume that developers can use hardware instrumentation and thus cannot evaluate software runningwithin emulators or virtual machines. Some prior modelsrequire actual energy measurements from the previous versions of applications in order to model the energy consumption of later versions of the same application.In this paper, we take a big-data approach to software energy consumption and present a model that can estimate software energy consumption mostly within 10% error (in joules) and does not require the developer to train on energy measurements of their own applications. This model leverages a big-data approach whereby a collection of prior applications’ energy measurements allows us to train, trans-mit, and apply the model to estimate any foreign application’s energy consumption for a test run. Our model is based on the dynamic traces of system calls and CPU utilization.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"17 1","pages":"49-60"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75362082","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}
Casimir Désarmeaux, Andrea Pecatikov, Shane McIntosh
Build systems describe how source code is translated into deliverables. Developers use build management tools like Maven to specify their build systems. Past work has shown that while Maven provides invaluable features (e.g., incremental building), it introduces an overhead on software development. Indeed, Maven build systems require maintenance. However, Maven build systems follow the build lifecycle, which is comprised of validate, compile, test, packaging, install, and deploy phases. Little is known about how build maintenance activity is dispersed among these lifecycle phases. To bridge this gap, in this paper, we analyze the dispersion of build maintenance activity across build lifecycle phases. Through analysis of 1,181 GitHub repositories that use Maven, we find that: (1) the compile phase accounts for 24% more of the build maintenance activity than the other phases; and (2) while the compile phase generates a consistent amount of maintenance activity over time, the other phases tend to generate peaks and valleys of maintenance activity. Software teams that use Maven should plan for these shifts in the characteristics of build maintenance activity.
{"title":"The Dispersion of Build Maintenance Activity across Maven Lifecycle Phases","authors":"Casimir Désarmeaux, Andrea Pecatikov, Shane McIntosh","doi":"10.1145/2901739.2903498","DOIUrl":"https://doi.org/10.1145/2901739.2903498","url":null,"abstract":"Build systems describe how source code is translated into deliverables. Developers use build management tools like Maven to specify their build systems. Past work has shown that while Maven provides invaluable features (e.g., incremental building), it introduces an overhead on software development. Indeed, Maven build systems require maintenance. However, Maven build systems follow the build lifecycle, which is comprised of validate, compile, test, packaging, install, and deploy phases. Little is known about how build maintenance activity is dispersed among these lifecycle phases. To bridge this gap, in this paper, we analyze the dispersion of build maintenance activity across build lifecycle phases. Through analysis of 1,181 GitHub repositories that use Maven, we find that: (1) the compile phase accounts for 24% more of the build maintenance activity than the other phases; and (2) while the compile phase generates a consistent amount of maintenance activity over time, the other phases tend to generate peaks and valleys of maintenance activity. Software teams that use Maven should plan for these shifts in the characteristics of build maintenance activity.","PeriodicalId":6621,"journal":{"name":"2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)","volume":"12 1","pages":"492-495"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85459727","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}
D. A. D. Costa, Shane McIntosh, U. Kulesza, A. Hassan
The release frequency of software projects has increased in recent years. Adopters of so-called rapid release cycles claim that they can deliver addressed issues (i.e., bugs, enhancements, and new features) to users more quickly. However, there is little empirical evidence to support these claims. In fact, in our prior work, we found that code integration phases may introduce delays in rapidly releasing software - 98% of addressed issues in the rapidly releasing Firefox project had their integration delayed by at least one release. To better understand the impact that rapid release cycles have on the integration delay of addressed issues, we perform a comparative study of traditional and rapid release cycles. Through an empirical study of 72,114 issue reports from the Firefox system, we observe that, surprisingly, addressed issues take a median of 50 days longer to be integrated in rapid Firefox releases than the traditional ones. To investigate the factors that are related to integration delay in traditional and rapid release cycles, we train regression models that explain if an addressed issue will have its integration delayed or not. Our explanatory models achieve good discrimination (ROC areas of 0.81-0.83) and calibration scores (Brier scores of 0.05-0.16). Deeper analysis of our explanatory models indicates that traditional releases prioritize the integration of backlog issues, while rapid releases prioritize issues that were addressed during the current release cycle. Our results suggest that rapid release cycles may not be a silver bullet for the rapid delivery of addressed issues to users.
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