Characterizing the differences between pre- and post- release versions of software

Li, Paul Luo; Kivett, Ryan; Zhan, Zhiyuan; Jeon, Sung-eok; Nagappan, Nachiappan; Murphy, Brendan; Ko, Andrew J.

doi:10.1145/1985793.1985894

Cited by 23 publications

(6 citation statements)

References 14 publications

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“…We found two kinds of data platform builders. Participants P1, P4, P8 and P11 work with on systems that involve platforms to collect in-field software failure data, including Windows Error Reporting [40] and Reliability Analysis Component [43]. Their work unites these data sources into a common platform to fit current business goals.…”

Section: Platform Buildersmentioning

confidence: 99%

The emerging role of data scientists on software development teams

Kim

Zimmermann

DeLine

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

200

145

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Creating and running software produces large amounts of raw data about the development process and the customer usage, which can be turned into actionable insight with the help of skilled data scientists. Unfortunately, data scientists with the analytical and software engineering skills to analyze these large data sets have been hard to come by; only recently have software companies started to develop competencies in software-oriented data analytics. To understand this emerging role, we interviewed data scientists across several product groups at Microsoft. In this paper, we describe their education and training background, their missions in software engineering contexts, and the type of problems on which they work. We identify five distinct working styles of data scientists: (1) Insight Providers, who work with engineers to collect the data needed to inform decisions that managers make; (2) Modeling Specialists, who use their machine learning expertise to build predictive models; (3) Platform Builders, who create data platforms, balancing both engineering and data analysis concerns; (4) Polymaths, who do all data science activities themselves; and (5) Team Leaders, who run teams of data scientists and spread best practices. We further describe a set of strategies that they employ to increase the impact and actionability of their work.

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Section: Platform Buildersmentioning

confidence: 99%

The emerging role of data scientists on software development teams

Kim

Zimmermann

DeLine

et al. 2016

Proceedings of the 38th International Conference on Software Engineering

200

145

View full text Add to dashboard Cite

show abstract

“…[45,75,31,48]). Comparatively, studies about post-deployment quality and dynamics have been less frequent [38,33]. However, a number of studies have looked at the aspects of software quality metrics, especially the quality perceived by the customers, e.g., [50,46,24,61,44].…”

Section: Related Workmentioning

confidence: 99%

Deriving a usage-independent software quality metric

Dey¹,

Mockus²

2020

Empir Software Eng

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Context: The extent of post-release use of software affects the number of faults, thus biasing quality metrics and adversely affecting associated decisions. The proprietary nature of usage data limited deeper exploration of this subject in the past. Objective: To determine how software faults and software use are related and how, based on that, an accurate quality measure can be designed. Method: Via Google Analytics we measure new users, usage intensity, usage frequency, exceptions, and release date and duration for complex proprietary mobile applications for Android and iOS. We utilize Bayesian Network and Random Forest models to explain the interrelationships and to derive the usage independent release quality measure. To increase external validity, we also investigate the interrelationship among various code complexity measures, usage (downloads), and number of issues for 520 NPM packages. We derived a usage-independent quality measure from these analyses, and applied it on 4430 popular NPM packages to construct timelines for comparing the perceived quality (number of issues) and our derived measure of quality during the lifetime of these packages. Results: We found the number of new users to be the primary factor determining the number of exceptions, and found no direct link between the intensity and frequency of software usage and software faults. Crashes increased with the power of 1.02-1.04 of new user for the Android app and power of 1.6 for the iOS app. Release quality expressed as crashes per user was independent of other usage-related predictors, thus serving as a usage independent measure of software quality. Usage also affected quality in NPM, where downloads were strongly associated with numbers of issues, even after taking the other code complexity measures into consideration. Unlike in mobile case where exceptions per user decrease over time, for 45.8% of the NPM packages the number of issues per download increase. Conclusions: We expect our result and our proposed quality measure will help gauge release quality of a software more accurately and inspire further research in this area.

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“…Many studies have looked at software code repositories in the past [23,25,28,40,46,50,65], most of which did not focus on synchronization issues. Some recent studies look at parallel programs written using Java concurrent programing constructs [44], MPI [33], and C# Task Parallel Library [40].…”

Section: Related Workmentioning

confidence: 99%

What change history tells us about thread synchronization

Jin

Song

et al. 2015

Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering

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Multi-threaded programs are pervasive, yet difficult to write. Missing proper synchronization leads to correctness bugs and over synchronization leads to performance problems. To improve the correctness and efficiency of multi-threaded software, we need a better understanding of synchronization challenges faced by real-world developers. This paper studies the code repositories of open-source multi-threaded software projects to obtain a broad and indepth view of how developers handle synchronizations. We first examine how critical sections are changed when software evolves by checking over 250,000 revisions of four representative open-source software projects. The findings help us answer questions like how often synchronization is an afterthought for developers; whether it is difficult for developers to decide critical section boundaries and lock variables; and what are real-world over-synchronization problems. We then conduct case studies to better understand (1) how critical sections are changed to solve performance problems (i.e. over-synchronization issues) and (2) how software changes lead to synchronization-related correctness problems (i.e. concurrency bugs). This in-depth study shows that tool support is needed to help developers tackle over-synchronization problems; it also shows that concurrency bug avoidance, detection, and testing can be improved through better awareness of code revision history.

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Characterizing the differences between pre- and post- release versions of software

Cited by 23 publications

References 14 publications

The emerging role of data scientists on software development teams

The emerging role of data scientists on software development teams

Deriving a usage-independent software quality metric

What change history tells us about thread synchronization

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