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Engineering Analytics platform, Hatica emerges from stealth with US$900,000 pre-seed funding - PRN India News

Engineering Analytics platform, Hatica emerges from stealth with US$900,000 pre-seed funding - PRN India News
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San Francisco , United States , Naomi Chopraand Haritabh Singh , Gaurav Chaturvedi , Naomi Chopra , Accel Founderstack Program , Software Engineering Analytics , Kae Capital , Titan Capital , Xseed Ventures , Accel Founderstack , Engineering Analytics ,

Engineering Analytics platform, Hatica emerges from stealth with US$900,000 pre-seed funding

Engineering Analytics platform,Hatica was founded by former Uber engineers Naomi Chopra and Haritabh Singh, who incubated the idea as part of the Accel Founders ....

San Francisco , United States , Haritabh Singh , Gaurav Chaturvedi , Naomi Chopra , Accel Founderstack Program , Software Engineering Analytics , Engineering Analytics , Accel Founderstack , Kae Capital , Software News , Oost Developer , Analytics Platform , Naomi Chopra , Rnd Well ,

Business News | Engineering Analytics Platform, Hatica Emerges from Stealth with US$900,000 Pre-seed Funding

Get latest articles and stories on Business at LatestLY. Gurugram (Haryana) [India]/ Engineering Analytics platform, Hatica has emerged from stealth with a $900,000 pre-seed funding, led by Kae Capital and followed by Titan Capital, iSeed Ventures, and angel investor GBS Bindra (CEO of Charmboard). Business News | Engineering Analytics Platform, Hatica Emerges from Stealth with US$900,000 Pre-seed Funding. ....

San Francisco , United States , Haritabh Singh , Gaurav Chaturvedi , Naomi Chopra , Read Flipkart Electronics Sale , Accel Founderstack Program , Software Engineering Analytics , Engineering Analytics , Kae Capital , Titan Capital , Xseed Ventures , Accel Founderstack , Rajasthan Shocker , Flipkart Electronics Sale , Get Massive ,

"Automatically recommending components for issue reports using deep lea" by Morakot Choetkiertikul, Hoa Khanh Dam et al.

Abstract
Today’s software development is typically driven by incremental changes made to software to implement a new functionality, fix a bug, or improve its performance and security. Each change request is often described as an issue. Recent studies suggest that a set of components (e.g., software modules) relevant to the resolution of an issue is one of the most important information provided with the issue that software engineers often rely on. However, assigning an issue to the correct component(s) is challenging, especially for large-scale projects which have up to hundreds of components. In this paper, we propose a predictive model which learns from historical issue reports and recommends the most relevant components for new issues. Our model uses Long Short-Term Memory, a deep learning technique, to automatically learn semantic features representing an issue report, and combines them with the traditional textual similarity features. An extensive evaluation on 142,025 ....

Long Short Term Memory , Component Recommendation , Deep Learning , Mining Software Repositories , Ecommendation Systems For Software Engineering , Software Engineering Analytics , நீண்டது குறுகிய கால நினைவு ,