Software log file analysis is involved heavily in both Software development and maintenance phases. It serves for various purposes such as verifying the conformance of the software functionality to the specification, software quality check and troubleshooting. Application log files or the logs generated by other monitoring tools are subjected to analysis for extracting information that can be vital in an investigation. These tasks demand expertise to a great deal and are labor intensive when performed manually. The lack of a commonly used technique to record expert knowledge stands as an impediment to automate the analysis tasks. The need for correlating information extracted from different locations in the same log file or multiple log files further ads to this complexity. This paper describes a framework based on mind maps which formulates a homogeneous platform for recording expert knowledge as well as for performing other tasks such as extracting information from log files, drawing inferences and creating reports. The framework includes a scripting language, a parallel application programming interface and a set of tools. Usage is illustrated by a proof of concept system built using the framework that creates a useful report after analyzing a log file generated by a widely used software monitoring tool.

Geographic Information Systems (GIS) provide inventory, analysis and visualization of geographic data to users of various levels. Users range from GIS experts who build models to analyze geographic patterns in data to general users such as travelers who need information on interesting places to visit and paths to follow. Popularity of social networks and ubiquitous use of hand held devices such as smartphones have brought the usage of GIS into the next level. With the Geo-location detection capabilities of hand held devices, users can share their current location and paths traversed along with useful metadata. An example would be a pedestrian taking a photograph of a badly conditioned place in the road and uploading it with Geo-location information for the consideration of authorities. As of today, a number of map data sources from different providers exist. Proprietary providers build their own maps and annotations and give it for a fee. Communities built map services, on the other hand, make the infrastructure available for a community to voluntarily contribute map data and consume them. This seems to be a promising approach given the convenience of getting networked and sharing geo-tagged information with the new technologies. This work focuses on the feasibility of implementing software infrastructure for a Community GIS driven by smartphone users with the current technology. Both smartphone native apps and mobile optimized web apps are explored and proof-of-concept apps are developed to demonstrate opportunities and challenges in each avenue.

Outsourced software development is a growing business model that has proven to bring cost-effective and efficient solutions for varying demands of a software product company. Though it has proven its capability in bringing increased market value to stay ahead of competition, there are few inherent problems commonly identified in practice. A prominent issue is how to verify the quality of the code/applications delivered to the customer. Given the fact that a critical bug leaking in to production can bring disastrous results, it is vital to ensure that the deliverables from the supplier conforms to a defined set of quality guidelines.

The work described in this paper is the design and implementation of a scalable software quality verification framework targeted towards an outsourcing supplier. The framework enables the supplier to build an industrial grade automated quality verification system, on top of which they may validate and ensure the quality of their deliverables before it reaches the customer.

The framework is capable of evaluating both at software code and software application levels. Code level evaluation is done in two phases; first is when the developer tries to add code to the repository (interactive commit stage) and secondly a deeper analysis covering a wide range of problems offline (noninteractive backend analysis). It is important that the rules used for evaluation, actions on results and alerting can be customized to suit the project context. When it comes to the application level, the framework provides a programming interface and a set of tools to verify the artifacts.

A case study quality verification system built using this framework proved to add a significant value to the deliverables of a commercial software project. An experiment done with the programming interface showed that powerful and complex analysis systems could be built to evaluate deliverables and even to aid in software due-diligence process.

Project MOINC (Mora Open Infrastructure for Network Computing) is an attempt to use processing power of idle computers when they are connected to a network to serve web service requests in a distributed computing architecture. This research paper considers the various aspects of grid computing architecture, clustering and load balancing aspects which have been optimized for distributed request processing of web services. Furthermore many techniques that have been utilized for implementation of volunteer computing model on the framework have also been described. The paper also describes the design of the whole system which can be easily extended in order to further optimization. The ultimate objective is to come up with more generalized grid computing framework for future web services.

Software log file analysis helps immensely in software testing and troubleshooting. The first step in automated log file analysis is extracting log data. This requires decoding the log file syntax and interpreting data semantics. The expected output of this phase is an organization of the extracted data for further processing. Log data extractors can be developed using popular programming languages targeting one or few log file formats. Rather than repeating this process for each log file format, it is desirable to have a generic scheme for interpreting elements of a log file and filling a data structure suitable for further processing. The new log data extraction scheme introduced in this paper is an attempt to provide the advanced features demanded by modern log file analysis procedures. It is a generic scheme which is capable of handling both text and binary log files with complex structures and difficult syntax. Its output is a tree filled with the information of interest for the particular case.