Browsing by Subject "Software metrics"
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Item Benchmarking tests on recovery oriented computing(2012-05) Raman, Nandita; Perry, Dewayne E.; Krasner, HerbBenchmarks have played a very important role in guiding the progress of computer science systems in various ways. Specifically, in Autonomous environments it has a major role to play. System crashes and software failures are a basic part of a software system’s life-cycle and to overcome or rather make it as less vulnerable as possible is the main purpose of recovery oriented computing. This is usually done by trying to reduce the downtime by automatically and efficiently recovering from a broad class of transient software failures without having to modify applications. There have been various types of benchmarks for recovering from a failure, but in this paper we intend to create a benchmark framework called the warning benchmarks to measure and evaluate the recovery oriented systems. It consists of the known and the unknown failures and few benchmark techniques which the warning benchmarks handle with the help of various other techniques in software fault analysis.Item An empirical study on software quality : developer perception of quality, metrics, and visualizations(2013-05) Wilson, Gary Lynn; Kim, MiryungSoftware tends to decline in quality over time, causing development and maintenance costs to rise. However, by measuring, tracking, and controlling quality during the lifetime of a software product, its technical debt can be held in check, reducing total cost of ownership. The measurement of quality faces challenges due to disagreement in the meaning of software quality, the inability to directly measure quality factors, and the lack of measurement practice in the software industry. This report addresses these challenges through both a literature survey, a metrics derivation process, and a survey of professional software developers. Definitions of software quality from the literature are presented and evaluated with responses from software professionals. A goal, question, metric process is used to derive quality-targeted metrics tracing back to a set of seven code-quality subgoals, while a survey to software professionals shows that despite agreement that metrics and metric visualizations would be useful for improving software quality, the techniques are underutilized in practice.Item Software metrics through fault data from empirical evaluation using verification & validation tools(2008-05) Garcia, Eric N.; Cooke, Daniel E.; Watson, Robert G.; Rushton, J. NelsonTraditional software metrics have been highly criticized for their inadequate measuring of software properties. Some metrics have been found to be based upon poor theoretical foundations (McCabe) while others are just too simplistic (Lines of Code). This thesis introduces a new methodology for creating software metrics. By defining metrics based upon where errors are typically discovered in code, with a particular emphasis on a composition of data and control flow, a new metric is proposed which measures the propensity of error prone areas in code. An empirical evaluation on software defects is used to identify error prone areas of source code. This evaluation uncovered characteristics shared by high error density code areas. Based on these common characteristics, a new software metric is defined. The new metric is then evaluated using a popular set of metric properties and validated using a statistical correlation.Item Usability and productivity for silicon debug software: a case study(2011-12) Singh, Punit; Krasner, Herb; Perry, Dewayne E.Semiconductor manufacturing is complex. Companies strive to lead in the markets by delivering timely chips which are bug (a.k.a defect) free and have very low power consumption. The new research drives new features in chips. The case study research reported here is about the usability and productivity of the silicon debug software tools. Silicon debug software tools are a set of software used to find bugs before delivering chips to the customer. The study has an objective to improve usability and productivity of the tools, by introducing metrics. The results of the measurements drive a concrete plan of action. The GQM (Goal, Questions, Metrics) methodology was used to define and gather data for the measurements. The project was developed in two parts or phases. We took the measurements using the method over the two phases of the tool development. The findings from phase one improved the tool usability in the second phase. The lesson learnt is that tool usability is a complex measurement. Improving usability means that the user will use less of the tool help button; the user will have less downtime and will not input incorrect data. Even though for this study the focus was on three important tools, the same usability metrics can be applied to the remaining five tools. For defining productivity metrics, we also used the GQM methodology. A productivity measurement using historic data was done to establish a baseline. The baseline measurements identified some existing bottlenecks in the overall silicon debug process. We link productivity to time it takes for a debug tool user to complete the assigned task(s). The total time taken for using all the tools does not give us any actionable items for improving productivity. We will need to measure the time it takes for use of each tool in the debug process to give us actionable items. This is identified as future work. To improve usability we recommend making tools that are more robust to error handling and having good help features. To improve productivity we recommend getting data on where the user is spending most of the debug time. Then, we can focus on improving that time-consuming part of debug to make the users more productive.