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Statistical Challenges
Pages 43-60

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From page 43... ... Software engineering combines application domain knowledge, computer science, statistics, behavioral science, and human factors issues. Statistical research and education challenges in software engineering involve the following: Generalizing particular experimental results to other settings and projects, Scaling up results obtained in academic studies to industrial settings, Combining information across software engineering projects and studies, Adopting exploratory data analysis and visualization techniques, Educating the software engineering community as to statistical approaches and data issues, Developing analysis methods to cope with qualitative vanables, Providing models with the appropriate error distributions for software engineering applications, and Improving accelerated life testing. Read the entire page →
From page 44... ... The capabilities of individuals strongly influence the metrics collected throughout the software production process. Capabilities include experience, intelligence' familiarity with the application domain, ability to communicate with others, ability to envision the problem spatially, and ability to verbally describe that spatial understanding. Read the entire page →
From page 45... ... The purpose of the software design and analysis experiment was to gather statistically valid evidence about the effect�on effort and quality�of using the principles of abstraction, encapsulation, and layering to enhance components of software systems. The experiment was divided into two types of tasks: I Read the entire page →
From page 46... ... ~ COMBINING INFORMATION The results of many diverse software projects and studies tend to lead to more confusion than insight. The software engineering community would benefit if more value were gained from the work that is being done. Read the entire page →
From page 47... ... Following the NRC recommendations on combining information across studies (NRC, 1992) , the appropriate model (the so-called random effects model in meta-analysis) Read the entire page →
From page 48... ... VISUALIZATION IN SOFTWARE ENGINEERING Scientific visualization is an emerging technology that is driven by ever-decreasing hardware prices and the associated increasing sophistication of visualization software. Visualization involves the interactive pictorial display of data using graphics, animation, and sound. Read the entire page →
From page 49... ... Configuration Management Data A rich software database suitable for visualization involves the code itself. In production systems, the source code is stored in configuration management databases. Read the entire page →
From page 50... ... One approach to improving the usefulness of function call graphs might involve the use of dynamic graphics techniques to focus the display on the visually informative regions. Test Code Coverage Another interesting example of source code visualization involves showing test suite code coverage. Read the entire page →
From page 51... ... Figure 7 displays the AT&T 5ESS switching code using the SeeSys� system, a dynamic graphics metrics visualization system. Interactive controls enable the user to manipulate the display, reset the colors, and zoom in on particular modules and files, providing an interactive software data analysis environment. Read the entire page →
From page 52... ... _. Much software data are nontraditional statistical data such as the change history of source code, duplication in manuals, or the structure of a relational database. Read the entire page →
From page 53... ... Function call graphs showing the Galling pattern between procedures. The fop pane] Read the entire page →
From page 55... ... There are two special colors: the black tines are non-executable lines of code such as variable declarations and comments, and the gray lines are the non-executed (not covered) lines. Read the entire page →
From page 57... ... , and modules within the subsystems. Color is used here to redundantly encode size according to the color scheme in the slider at the bottom of the screen. Read the entire page →
From page 59... ... performance data including function calling patterns, line execution counts, operating system page faults, heap usage, and stack space, as well as disk usage. Novel techniques to understand and digest dynamic program execution data would be immediately useful. Read the entire page →
From page 60... ... Proper measurement protocols may diminish such multipropagation. Finally, given good-quality data, it may be possible to extend orthogonal defect classification to efforts to identify risks in the production of software, perhaps using data to provide early indicators of product quality and potential problems concerning scheduling. Read the entire page →

From page 43...

... Software engineering combines application domain knowledge, computer science, statistics, behavioral science, and human factors issues. Statistical research and education challenges in software engineering involve the following: Generalizing particular experimental results to other settings and projects, Scaling up results obtained in academic studies to industrial settings, Combining information across software engineering projects and studies, Adopting exploratory data analysis and visualization techniques, Educating the software engineering community as to statistical approaches and data issues, Developing analysis methods to cope with qualitative vanables, Providing models with the appropriate error distributions for software engineering applications, and Improving accelerated life testing.

Statistical Challenges Pages 43-60

Statistical Challenges
Pages 43-60