Add some recent readings

Miltos Allamanis · Miltos Allamanis · commit a9c69150b7de · 2021-04-12T06:43:17.000+01:00
diff --git a/_publications/gholamian2021naturalness.markdown b/_publications/gholamian2021naturalness.markdown
@@ -0,0 +1,26 @@
+---
+layout: publication
+title: "On the Naturalness and Localness of Software Logs"
+authors: Sina Gholamian, Paul A. S. Ward
+conference:
+year: 2021
+bibkey: gholamian2021naturalness
+tags: ["logging", "language model"]
+---
+Logs are an essential part  of  the  development  and
+maintenance  of  large  and  complex  software  systems  as  they
+contain rich information pertaining to the dynamic content and
+state  of  the  system.  As  such,  developers  and  practitioners  rely
+heavily  on  the  logs  to  monitor  their  systems.  In  parallel,  the
+increasing  volume  and  scale  of  the  logs,  due  to  the  growing
+complexity  of  modern  software  systems,  renders  the  traditional
+way  of  manual  log  inspection  insurmountable.  Consequently,  to
+handle  large  volumes  of  logs  efficiently  and  effectively,  various
+prior research aims to automate the analysis of log files. Thus, in
+this paper, we begin with the hypothesis that log files are natural
+and local and these attributes can be applied for automating log
+analysis tasks. We guide our research with six research questions
+with regards to the naturalness and localness of the log files, and
+present  a  case  study  on  anomaly  detection  and  introduce  a  tool
+for anomaly detection, called ANALOG, to demonstrate how our
+new  findings  facilitate  the  automated  analysis  of  logs.
diff --git a/_publications/haldar2020multiperspective.markdown b/_publications/haldar2020multiperspective.markdown
@@ -0,0 +1,12 @@
+---
+layout: publication
+title: "A Multi-Perspective Architecture for Semantic Code Search"
+authors: Rajarshi Haldar, Lingfei Wu, Jinjun Xiong, Julia Hockenmaier
+conference: ACL
+year: 2020
+bibkey: haldar2020multiperspective
+additional_links:
+   - {name: "ArXiV", url: "https://arxiv.org/abs/2005.06980"}
+tags: ["search"]
+---
+The ability to match pieces of code to their corresponding natural language descriptions and vice versa is fundamental for natural language search interfaces to software repositories. In this paper, we propose a novel multi-perspective cross-lingual neural framework for code--text matching, inspired in part by a previous model for monolingual text-to-text matching, to capture both global and local similarities. Our experiments on the CoNaLa dataset show that our proposed model yields better performance on this cross-lingual text-to-code matching task than previous approaches that map code and text to a single joint embedding space. 
