What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing

TL;DR

This paper introduces an automated cause analysis model for test alarms in system and integration testing, leveraging information retrieval to reduce manual effort and improve accuracy on industrial datasets.

Contribution

The paper presents a novel automated cause analysis approach that outperforms baseline algorithms and is efficiently deployable in industrial settings.

Findings

Achieves up to 65.8% accuracy on datasets

Outperforms baseline algorithms by up to 13.3%

Deployed in industry with 72% accuracy after two months

Abstract

Driven by new software development processes and testing in clouds, system and integration testing nowadays tends to produce enormous number of alarms. Such test alarms lay an almost unbearable burden on software testing engineers who have to manually analyze the causes of these alarms. The causes are critical because they decide which stakeholders are responsible to fix the bugs detected during the testing. In this paper, we present a novel approach that aims to relieve the burden by automating the procedure. Our approach, called Cause Analysis Model, exploits information retrieval techniques to efficiently infer test alarm causes based on test logs. We have developed a prototype and evaluated our tool on two industrial datasets with more than 14,000 test alarms. Experiments on the two datasets show that our tool achieves an accuracy of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per cause analysis. Due to the attractive experimental results, our industrial partner, a leading information and communication technology company in the world, has deployed the tool and it achieves an average accuracy of 72% after two months of running, nearly three times more accurate than a previous strategy based on regular expressions.