Infrastructure for Building Code Search Applications for Developers

Convective Storms and the Insurance Industry: Challenges and Opportunities

The Oxford Handbook of Non-Synoptic Wind Storms ◽

10.1093/oxfordhb/9780190670252.013.9 ◽

2020 ◽

Author(s):

Kirsten D. Orwig

Keyword(s):

United States ◽

Tropical Cyclones ◽

Insurance Industry ◽

Property Values ◽

Construction Materials ◽

The United States ◽

Building Code ◽

Convective Storms ◽

Challenges And Opportunities ◽

Insured Loss

Convective storms affect countries worldwide, with billions in losses and dozens of fatalities every year. They are now the key insured loss driver in the United States, even after considering the losses sustained by tropical cyclones in 2017. Since 2008, total insured losses from convective storms have exceeded $10 billion per year. Additionally, these losses continue to increase year over year. Key loss drivers include increased population, buildings, vehicles, and property values. However, other loss drivers relate to construction materials and practices, as well as building code adoption and enforcement. The increasing loss trends pose a number of challenges for the insurance industry and broader society. These challenges are discussed, and some recommendations are presented.

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Building Code, Wind Contours, and House Prices

Journal of Real Estate Research ◽

10.1080/10835547.2012.12091321 ◽

2012 ◽

Vol 34 (1) ◽

pp. 73-98

Author(s):

Randy Dumm ◽

G. Stacy Sirmans ◽

Greg Smersh

Keyword(s):

House Prices ◽

Building Code

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Guided pattern mining for API misuse detection by change-based code analysis

Automated Software Engineering ◽

10.1007/s10515-021-00294-x ◽

2021 ◽

Vol 28 (2) ◽

Author(s):

Sebastian Nielebock ◽

Robert Heumüller ◽

Kevin Michael Schott ◽

Frank Ortmeier

Keyword(s):

Pattern Mining ◽

Third Party ◽

Just In Time ◽

False Alarms ◽

Misuse Detection ◽

Code Search ◽

Usage Patterns ◽

Development Processes ◽

Code Changes ◽

Api Usage

AbstractLack of experience, inadequate documentation, and sub-optimal API design frequently cause developers to make mistakes when re-using third-party implementations. Such API misuses can result in unintended behavior, performance losses, or software crashes. Therefore, current research aims to automatically detect such misuses by comparing the way a developer used an API to previously inferred patterns of the correct API usage. While research has made significant progress, these techniques have not yet been adopted in practice. In part, this is due to the lack of a process capable of seamlessly integrating with software development processes. Particularly, existing approaches do not consider how to collect relevant source code samples from which to infer patterns. In fact, an inadequate collection can cause API usage pattern miners to infer irrelevant patterns which leads to false alarms instead of finding true API misuses. In this paper, we target this problem (a) by providing a method that increases the likelihood of finding relevant and true-positive patterns concerning a given set of code changes and agnostic to a concrete static, intra-procedural mining technique and (b) by introducing a concept for just-in-time API misuse detection which analyzes changes at the time of commit. Particularly, we introduce different, lightweight code search and filtering strategies and evaluate them on two real-world API misuse datasets to determine their usefulness in finding relevant intra-procedural API usage patterns. Our main results are (1) commit-based search with subsequent filtering effectively decreases the amount of code to be analyzed, (2) in particular method-level filtering is superior to file-level filtering, (3) project-internal and project-external code search find solutions for different types of misuses and thus are complementary, (4) incorporating prior knowledge of the misused API into the search has a negligible effect.

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