Biblio

Context:

The ‘as code’ suffix in infrastructure as code (IaC) refers to applying software engineering activities, such as version control, to maintain IaC scripts. Without the application of these activities, defects that can have serious consequences may be introduced in IaC scripts. A systematic investigation of the development anti-patterns for IaC scripts can guide practitioners in identifying activities to avoid defects in IaC scripts. Development anti-patterns are recurring development activities that relate with defective IaC scripts.

Goal:

The goal of this paper is to help practitioners improve the quality of infrastructure as code (IaC) scripts by identifying development activities that relate with defective IaC scripts.

Methodology:

We identify development anti-patterns by adopting a mixed-methods approach, where we apply quantitative analysis with 2,138 open source IaC scripts and conduct a survey with 51 practitioners.

Findings:

We observe five development activities to be related with defective IaC scripts from our quantitative analysis. We identify five development anti-patterns namely, ‘boss is not around’, ‘many cooks spoil’, ‘minors are spoiler’, ‘silos’, and ‘unfocused contribution’.

Conclusion:

Our identified development anti-patterns suggest the importance of ‘as code’ activities in IaC because these activities are related to quality of IaC scripts.

Defects in infrastructure as code (IaC) scripts can have serious
consequences, for example, creating large-scale system outages. A
taxonomy of IaC defects can be useful for understanding the nature
of defects, and identifying activities needed to fix and prevent
defects in IaC scripts. The goal of this paper is to help practitioners
improve the quality of infrastructure as code (IaC) scripts by developing
a defect taxonomy for IaC scripts through qualitative analysis.
We develop a taxonomy of IaC defects by applying qualitative analysis
on 1,448 defect-related commits collected from open source
software (OSS) repositories of the Openstack organization. We conduct
a survey with 66 practitioners to assess if they agree with the
identified defect categories included in our taxonomy. We quantify
the frequency of identified defect categories by analyzing 80,425
commits collected from 291 OSS repositories spanning across 2005
to 2019.

Our defect taxonomy for IaC consists of eight categories, including
a category specific to IaC called idempotency (i.e., defects that
lead to incorrect system provisioning when the same IaC script is
executed multiple times). We observe the surveyed 66 practitioners
to agree most with idempotency. The most frequent defect category
is configuration data i.e., providing erroneous configuration data
in IaC scripts. Our taxonomy and the quantified frequency of the
defect categories may help in advancing the science of IaC script
quality.

Android applications pose security and privacy risks for end-users. These risks are often quantified by performing dynamic analysis and permission analysis of the Android applications after release. Prediction of security and privacy risks associated with Android applications at early stages of application development, e.g. when the developer (s) are
writing the code of the application, might help Android application developers in releasing applications to end-users that have less security and privacy risk. The goal of this paper
is to aid Android application developers in assessing the security and privacy risk associated with Android applications by using static code metrics as predictors. In our paper, we consider security and privacy risk of Android application as how susceptible the application is to leaking private information of end-users and to releasing vulnerabilities. We investigate how effectively static code metrics that are extracted from the source code of Android applications, can be used to predict security and privacy risk of Android applications. We collected 21 static code metrics of 1,407 Android applications, and use the collected static code metrics to predict security and privacy risk of the applications. As the oracle of security and privacy risk, we used Androrisk, a tool that quantifies the amount of security and privacy risk of an Android application using analysis of Android permissions and dynamic analysis. To accomplish our goal, we used statistical learners such as, radial-based support vector machine (r-SVM). For r-SVM, we observe a precision of 0.83. Findings from our paper suggest that with proper selection of static code metrics, r-SVM can be used effectively to predict security and privacy risk of Android applications