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.

UNDERSTANDING ESSAY RATING AS A SOCIALLY MEDIATED ACTIVITY: THE CASE OF A HIGH-STAKES ENGLISH TEST IN CHINA

Most essay rating research in language assessment has examined human raters’ essay rating as a cognitive process, thus overlooking or oversimplifying the interaction between raters and sociocultural contexts. Given that raters are social beings, their practices have social meanings and consequences. Hence it is important to situate essay rating within its sociocultural context for a more meaningful understanding. Drawing on Engeström’s (1987, 2001) cultural-historical activity theory (CHAT) framework with a sociocultural perspective, this study reconceptualized essay rating as a socially mediated activity with both cognitive (individual raters’ goal-directed decision-making actions) and social layers (raters’ collective object-oriented essay rating activity at related settings). In particular, this study explored raters’ essay rating at one provincial rating centre in China within the context of a high-stakes university entrance examination, the National Matriculation English Test (NMET). This study adopted a multiple-method multiple-perspective qualitative case study design. Think-aloud protocols, stimulated recalls, interviews, and documents served as the data sources. This investigation involved 25 participants at two settings (rating centre and high schools), including rating centre directors, team leaders, NMET essay raters who were high school teachers, and school principals and teaching colleagues of these essay raters. Data were analyzed using Strauss and Corbin’s (1990) open and axial coding techniques, and CHAT for data integration. The findings revealed the interaction between raters and the NMET sociocultural context. Such interaction can be understood through a surface structure (cognitive layer) and a deep structure (social layer) concerning how raters assessed NMET essays, where the surface structure reflected the “what” and the deep structure explained the “how” and “why” in raters’ decision-making. This study highlighted the roles of goals and rules in rater decision-making, rating tensions and raters’ solutions, and the relationship between essay rating and teaching. This study highlights the value of a sociocultural view to essay rating research, demonstrates CHAT as a sociocultural approach to investigate essay rating, and proposes a direction for future washback research on the effect of essay rating. This study also provides support for NMET rating practices that can potentially bring positive washback to English teaching in Chinese high schools.