Predicting risk of software changes

By Audris Mockus, D. Weiss. Bell Labs technical journal, 2000

436 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/2be4c677558a6ed18cef287f5580e4b9fbaf108b

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Citations in the corpus

The impact of code review coverage and code review participation on software quality: a case study of the qt, VTK, and ITK projects (2014, background): “, defects in official releases of a software product), which is a popular proxy for software quality [5, 13, 18, 27, 30].”
Expertise Browser: a quantitative approach to identifying expertise (2002, background): “A previous study [19] examined the relationship between EAs and the probability that a software delivery would cause a failure after deployment.”
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017, background): “[74] point out that the appropriate range of probability values is important to make an appropriate decision (e.”
An empirical study of the impact of modern code review practices on software quality (2015, background): “Changes produced by novice developers are more likely to introduce defects than those produced by subject matter experts (Mockus and Weiss, 2000).”
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, background): “various software metrics on the defect-proneness of a module [9], [10], [11], [12], [13], [14].”
How, and why, process metrics are better (2013, background): “All these metrics are drawn from prior research [2, 4, 17, 20].”
An Empirical Study of Speed and Communication in Globally Distributed Software Development (2003, methodology): “By performing simple calculations on the MR data, it is possible to derive several important measures (e.g., [33]), described below.”
Don't touch my code!: examining the effects of ownership on software quality (2011, methodology): “Mockus and Weiss used properties of an individual change to predict the probability of that change causing a failure [23].”
The New C Standard (Excerpted material) An Economic and Cultural Commentary (2008, neutral): “Another study at Bell Labs[305] found that the probability of a fault being introduced into an application, during an update, correlated with the experience of the developer doing the work.”
Gender differences and bias in open source: pull request acceptance of women versus men (2017, background): “However, this theory is not consistent with observation 4, because women tend to change more lines of code, and changing more lines of code correlates with an increased risk of introducing bugs [21].”
An empirical study of global software development: distance and speed (2001, background): “, [24]), such as the following: Work interval.”
A large-scale empirical study of just-in-time quality assurance (2013, methodology): “Only Kim et al. and Mockus and Weiss considered the prediction at the commit level [26], [41].”
Deep Learning for Just-in-Time Defect Prediction (2015, background): “predict defects at change-level in a telecommunication system [19].”
Classifying Software Changes: Clean or Buggy? (2008, methodology): “Mockus and Weiss predict risky modules in software by using a regression algorithm and change measures such as the number of systems touched, the number of modules touched, the number of lines of added code, and the number of modification requests [33].”