Analyzing software data bindings in large-scale systems

One central feature of the structure of a software system is the coupling among its components (e.g., subsystems. modules) and the cohesion within them. The purpose of this study is to quantify ratios of coupling and cohesion and use them in the generation of hierarchical system descriptions. The ability of the hierarchical descriptions to localize errors by identifying error-prone system structure is evaluated using actual error data. Measures of data interaction, called data bindings, are used as the basis for calculating software coupling and cohesion. A 135,000 source line system from a production environment has been selected for empirical analysis. Software error data was collected from high-level system design through system test and from some field operation of the system. A set of five tools is applied to calculate the data bindings automatically, and cluster analysis is used to determine a hierarchical description of each of the system's 77 subsystems. An analysis of variance model is used to characterize subsystems and individual routines that had either many/few errors or high/low error correction effort

Calculation and use of an environment's characteristic software metric set

Since both cost/quality and production environments differ, this study presents an approach for customizing a characteristic set of software metrics to an environment. The approach is applied in the Software Engineering Laboratory (SEL), a NASA Goddard production environment, to 49 candidate process and product metrics of 652 modules from six (51,000 to 112,000 lines) projects. For this particular environment, the method yielded the characteristic metric set (source lines, fault correction effort per executable statement, design effort, code effort, number of I/O parameters, number of versions). The uses examined for a characteristic metric set include forecasting the effort for development, modification, and fault correction of modules based on historical data

Which Modality Best Promotes Student Achievement in College Algebra

The exploration of three modalities (Face-to-Face instruction, Hybrid instruction, and Online Only instruction) of learning College Algebra was the purpose of this study. The classes for this study were from the same instructor, during the same semester, during restrictions due to the Covid-19 virus in 2020. The Face-to-Face instruction had 150 minutes of class instruction each week. The Hybrid class had 75 minutes of class instruction each week and the Online Only students had no classroom instruction each week. There were 21 students in the face-to-face class, 32 students in the hybrid section, and 66 students in the online section. The final grades of the three modalities were compared and the null hypothesis was that the medians would be the same across all of the modalities of instruction. The results of this study showed that while the final average median score for the Face-to-Face class and the Online Only were close, the Hybrid class was significantly less than either the Face-to-Face or the Online Only and the null hypothesis was rejected. However, when students with one or more months of inactivity in the course were eliminated, the medians were not significantly different and the null hypothesis failed to be rejected. The results are discussed with the focal point to determine if one modality is better than another to predict the success of students in College Algebra

Sugar beet investigations in Ohio in 1900

Caption title.Mode of access: Internet

Will It be a Tough Year?

While there may be some glimmers of hope about a turnaround to the U.S. economy, such as decreasing unemployment, Virginia CPAs aren\u27t betting on a huge recovery in the next year. The second annual VSCPA Economic Outlook Survey reveals Virginia CPAs are more pessimistic about the United States and Virginia economies than they were last year, but they are actually more optimistic about Virginia\u27s economy compared to the United States as a whole. Likewise, VSCPA members continue to feel good about the economic outlook in Virginia relative to neighboring states

Databook for human factors engineers. Volume 2 - Common formulas, metrics, definitions

Human factors engineering manual including mathematical formulas, nomographs, conversion tables, units of measurement, and nomenclature