SABER: smart analysis based error reduction

• Authors: Darrell Reimer;Edith Schonberg;Kavitha Srinivas;Harini Srinivasan;Bowen Alpern;Robert D. Johnson;Aaron Kershenbaum;Larry Koved
• Venue: ISSTA '04 Proceedings of the 2004 ACM SIGSOFT international symposium on Software testing and analysis
• Year: 2004

In this paper, we present an approach to automatically detect high impact coding errors in large Java applications which use frameworks. These high impact errors cause serious performance degradation and outages in real world production environments, are very time-consuming to detect, and potentially cost businesses thousands of dollars. Based on 3 years experience working with IBM customer production systems, we have identified over 400 high impact coding patterns, from which we have been able to distill a small set of pattern detection algorithms. These algorithms use deep static analysis, thus moving problem detection earlier in the development cycle from production to development. Additionally, we have developed an automatic false positive filtering mechanism based on domain specific knowledge to achieve a level of usability acceptable to IBM field engineers. Our approach also provides necessary contextual information around the sources of the problems to help in problem remediation. We outline how our approach to problem determination can be extended to multiple programming models and domains. We have implemented this problem determination approach in the SABER tool and have used it successfully to detect many serious code defects in several large commercial applications. This paper shows results from four such applications that had over 60 coding defects.