BARRIERS AND SOLUTIONS TO OPEN SOURCE APPROPRIATE TECHNOLOGY FOR SUSTAINABLE DEVELOPMENT: INNOVATION THROUGH COLLABORATION

It has been proposed that the field of appropriate technology (AT) - small-scale, energy efficient and low-cost solutions, can be of tremendous assistance in many of the sustainable development challenges, such as food and water security, health, shelter, education and work opportunities. Unfortunately, there has not yet been a significant uptake of AT by organizations, researchers, policy makers or the mainstream public working in the many areas of the development sector. Some of the biggest barriers to higher AT engagement include: 1) AT perceived as inferior or ‘poor persons technology’, 2) questions of technological robustness, design, fit and transferability, 3) funding, 4) institutional support, as well as 5) general barriers associated with tackling rural poverty. With the rise of information and communication technologies (ICTs) for online networking and knowledge sharing, the possibilities to tap into the collaborative open-access and open-source AT are growing, and so is the prospect for collective poverty reducing strategies, enhancement of entrepreneurship, communications, education and a diffusion of life-changing technologies. In short, the same collaborative philosophy employed in the success of open source software can be applied to hardware design of technologies to improve sustainable development efforts worldwide. To analyze current barriers to open source appropriate technology (OSAT) and explore opportunities to overcome such obstacles, a series of interviews with researchers and organizations working in the field of AT were conducted. The results of the interviews confirmed the majority of literature identified barriers, but also revealed that the most pressing problem for organizations and researchers currently working in the field of AT is the need for much better communication and collaboration to share the knowledge and resources and work in partnership. In addition, interviews showcased general receptiveness to the principles of collaborative innovation and open source on the ground level. A much greater focus on networking, collaboration, demand-led innovation, community participation, and the inclusion of educational institutions through student involvement can be of significant help to build the necessary knowledge base, networks and the critical mass exposure for the growth of appropriate technology.

An Exploration of the challenges associated with software logging in large systems

Over the past few years, logging has evolved from from simple printf statements to more complex and widely used logging libraries. Today logging information is used to support various development activities such as fixing bugs, analyzing the results of load tests, monitoring performance and transferring knowledge. Recent research has examined how to improve logging practices by informing developers what to log and where to log. Furthermore, the strong dependence on logging has led to the development of logging libraries that have reduced the intricacies of logging, which has resulted in an abundance of log information. Two recent challenges have emerged as modern software systems start to treat logging as a core aspect of their software. In particular, 1) infrastructural challenges have emerged due to the plethora of logging libraries available today and 2) processing challenges have emerged due to the large number of log processing tools that ingest logs and produce useful information from them. In this thesis, we explore these two challenges. We first explore the infrastructural challenges that arise due to the plethora of logging libraries available today. As systems evolve, their logging infrastructure has to evolve (commonly this is done by migrating to new logging libraries). We explore logging library migrations within Apache Software Foundation (ASF) projects. We i find that close to 14% of the pro jects within the ASF migrate their logging libraries at least once. For processing challenges, we explore the different factors which can affect the likelihood of a logging statement changing in the future in four open source systems namely ActiveMQ, Camel, Cloudstack and Liferay. Such changes are likely to negatively impact the log processing tools that must be updated to accommodate such changes. We find that 20%-45% of the logging statements within the four systems are changed at least once. We construct random forest classifiers and Cox models to determine the likelihood of both just-introduced and long-lived logging statements changing in the future. We find that file ownership, developer experience, log density and SLOC are important factors in determining the stability of logging statements.

Supporting Run-time Monitoring of UML-RT through Customizable Monitoring Configurations in PapyrusRT

Model Driven Engineering uses the principle that code can automatically be generated from software models which would potentially save time and cost of development. By this methodology, a systems structure and behaviour can be expressed in more abstract, high level terms without some of the accidental complexity that the use of a general purpose language can bring. Models are the actual implementation of the system unlike in traditional software development where models are often used for documentation purposes only. However once the code is generated from the model, testing and debugging activities tend to happen on the code level and the model is not updated. We believe that monitoring on the model level could potentially facilitate quality assurance activities as the errors are detected in the early phase of development. In this thesis, we create a Monitoring Configuration for an open source model driven engineering tool called PapyrusRT in Eclipse. We support the run-time monitoring of UML-RT elements with a tracing tool called LTTng. We annotate the model with monitoring information to be used by the code generator for adding tracepoint statements for the corresponding elements. We provide the option of a timing specification to discover latency errors on the model. We validate the results by creating and tracing real time models in PapyrusRT.

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.