IMPROVING THE PERFORMANCE OF DATABASE-CENTRIC APPLICATIONS THROUGH PROGRAM ANALYSIS

Modern software applications are becoming more dependent on database management systems (DBMSs). DBMSs are usually used as black boxes by software developers. For example, Object-Relational Mapping (ORM) is one of the most popular database abstraction approaches that developers use nowadays. Using ORM, objects in Object-Oriented languages are mapped to records in the database, and object manipulations are automatically translated to SQL queries. As a result of such conceptual abstraction, developers do not need deep knowledge of databases; however, all too often this abstraction leads to inefficient and incorrect database access code. Thus, this thesis proposes a series of approaches to improve the performance of database-centric software applications that are implemented using ORM. Our approaches focus on troubleshooting and detecting inefficient (i.e., performance problems) database accesses in the source code, and we rank the detected problems based on their severity. We first conduct an empirical study on the maintenance of ORM code in both open source and industrial applications. We find that ORM performance-related configurations are rarely tuned in practice, and there is a need for tools that can help improve/tune the performance of ORM-based applications. Thus, we propose approaches along two dimensions to help developers improve the performance of ORM-based applications: 1) helping developers write more performant ORM code; and 2) helping developers configure ORM configurations. To provide tooling support to developers, we first propose static analysis approaches to detect performance anti-patterns in the source code. We automatically rank the detected anti-pattern instances according to their performance impacts. Our study finds that by resolving the detected anti-patterns, the application performance can be improved by 34% on average. We then discuss our experience and lessons learned when integrating our anti-pattern detection tool into industrial practice. We hope our experience can help improve the industrial adoption of future research tools. However, as static analysis approaches are prone to false positives and lack runtime information, we also propose dynamic analysis approaches to further help developers improve the performance of their database access code. We propose automated approaches to detect redundant data access anti-patterns in the database access code, and our study finds that resolving such redundant data access anti-patterns can improve application performance by an average of 17%. Finally, we propose an automated approach to tune performance-related ORM configurations using both static and dynamic analysis. Our study shows that our approach can help improve application throughput by 27--138%. Through our case studies on real-world applications, we show that all of our proposed approaches can provide valuable support to developers and help improve application performance significantly.

Analysis of Poverty Dynamics in Canada (Working Paper 9)

While we know much about poverty (or “low income”) in Canada in a static context, our understanding of the underlying dynamics remains very limited. This is particularly problematic from a policy perspective and the country has been increasingly left out on an international level in this regard. The contribution of this paper is to report the results of an empirical analysis of low income (“poverty”) dynamics in Canada using the recently available “LAD” tax-based database. The paper first describes the general nature of individuals’ poverty profiles (how many are short-term versus longterm, etc.)., the breakdown of the poor population in any given year amongst these different types, and the characterisation of poverty profiles by sex and family type. It then reports the estimation of various econometric models, starting with a set which specifies entry into and exit from poverty in any given year as a function of a variety of personal attributes and situational characteristics, including family status and changes therein, province of residence, inter-provincial mobility, language, area size of residence and calendar year (to capture trend effects). A set of proper hazard models then adds duration effects to these specifications to see how exit and re-entry probabilities shift with the amount of time spent in a poverty spell or after having exited a previous spell. A final set of specifications then investigates “occurrence dependence” effects by including past poverty spells first in an entry model and then with respect to the probability of being poor in a given year. Policy implications are discussed.