Do Dynamic Object Process Graphs Support Program Understanding? - A Controlled Experiment.

Quante, Jochen

doi:10.1109/icpc.2008.15

Cited by 34 publications

(17 citation statements)

References 7 publications

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“…The solutions given by the Ecl+Ext subjects were 43.38% more accurate (Table IV), averaging 17.88 out of 24 points compared to 12.47 points for the Eclipse group. 9 Related studies point out that it is not uncommon for several tasks to remain unfinished during the actual experiments (e.g., [48] and [40]). Similar to the timing data, the requirements for the use of the parametric t-test were met.…”

Section: B Correctness Resultsmentioning

confidence: 99%

A Controlled Experiment for Program Comprehension through Trace Visualization

Cornelissen

Zaidman

Deursen

2011

IIEEE Trans. Software Eng.

114

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Software maintenance activities require a sufficient level of understanding of the software at hand that unfortunately is not always readily available. Execution trace visualization is a common approach in gaining this understanding, and among our own efforts in this context is EXTRAVIS, a tool for the visualization of large traces. While many such tools have been evaluated through case studies, there have been no quantitative evaluations to the present day. This paper reports on the first controlled experiment to quantitatively measure the added value of trace visualization for program comprehension.We designed eight typical tasks aimed at gaining an understanding of a representative subject system, and measured how a control group (using the Eclipse IDE) and an experimental group (using both Eclipse and EXTRAVIS) performed in terms of correctness and time spent. The results are statistically significant in both regards, showing a 22% decrease in time needed for the given tasks, and a 43% increase in correctness of the results for the group using trace visualization. Index TermsProgram comprehension, dynamic analysis, controlled experiment.B. Cornelissen is with the Software Improvement Group, A.J.

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Section: B Correctness Resultsmentioning

confidence: 99%

A Controlled Experiment for Program Comprehension through Trace Visualization

Cornelissen

Zaidman

Deursen

2011

IIEEE Trans. Software Eng.

114

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“…Other researchers also conducted controlled experiments to validate tools supporting software maintenance tasks: Cornelissen et al [5] evaluated a trace visualizing tool with 24 student subjects. Quante et al [18] evaluated with 25 students the benefits of Dynamic Object Process Graphs (DOPGs) for program comprehension.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Dynamic Information in IDEs Improves Speed and Correctness of Software Maintenance Tasks

Röthlisberger

Harry

Binder

et al. 2012

IIEEE Trans. Software Eng.

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Modern IDEs such as Eclipse offer static views of the source code, but such views ignore information about the run-time behavior of software systems. Since typical object-oriented systems make heavy use of polymorphism and dynamic binding, static views will miss key information about the run-time architecture. In this paper we show by means of a controlled experiment with 30 professional developers that for typical software maintenance tasks integrating dynamic information into the Eclipse IDE yields a significant 17.5% decrease of time spent while significantly increasing the correctness of the solutions by 33.5%. Furthermore, we describe several enhancements to the Eclipse IDE that integrate static and dynamic information, with the goal of better supporting typical software maintenance activities. We elaborate on a case study which further highlights the usefulness of dynamic information for performance optimizations. We also report on several important efficiency improvements to our dynamic information collection framework, and we present benchmarks evaluating the overhead of our approach.

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“…Include more than one subject system in the experimental design. The experiment of Quante [Qua08] showed that performing the same experiment on two different systems can lead to significantly different results. Therefore, in our experiment, we consider two systems, different in both scale and application domain.…”

Section: Wish List Extracted From the Literaturementioning

confidence: 99%

“…In spite of the increased complexity in organizing the experiment and analyzing the data, introduced by a second independent variable, we chose to perform the experiment with two different object systems. Besides our interest in analyzing the effect of the object system size on the performance of CodeCity's users, we also applied the lessons learned from Quante's experiment [Qua08] that the results obtained on a single object system are not reliable. The two object systems we opted for are well-known open-source systems of different, realistic sizes (see Table 7.2) and of orthogonal application domains.…”

Section: External Validitymentioning

confidence: 99%

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Software systems as cities

Wettel

Lanza

Robbes

2011

Proceedings of the 33rd International Conference on Software Engineering

149

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Software understanding takes up a large share of the total cost of a software system. The high costs attributed to software understanding activities are caused by the size and complexity of software systems, by the continuous evolution that these systems are subject to, and by the lack of physical presence which makes software intangible. Reverse engineering helps practitioners deal with the intrinsic complexity of software, by providing a broad range of patterns and techniques. One of these techniques is software visualization, which makes software more tangible, by providing visible representations of software systems.Interpreting a visualization is by no means trivial and requires knowledge about the visual language of the visualization. One means to ease the learning of a new visualization's language are metaphors, which allow the interpretation of new data representations by analogy. Possibly one of the most popular metaphors for software visualization is the city metaphor, which has been explored in the past by a number of researchers. However, in spite of the efforts, the value of this metaphor for reverse engineering has never been taken beyond anecdotical evidence.In this dissertation, we demonstrate the value of the city metaphor for reverse engineering along two directions. On the one hand, we show that the metaphor is versatile enough to allow the representation of different facets of software. On the other hand, we show that the city metaphor enables the creation of software visualizations which efficiently and effectively support reverse engineering activities.Our interpretation of the city metaphor at its core depicts the system as a city, the packages as districts, and the classes as buildings. The resulting "code city" visualization provides a structural overview of the software system, enriched with contextual data. To be able to perform analyses of real systems using our approach, we implemented a tool called CodeCity.We demonstrate the versatility of the metaphor, by using it in three different analysis contexts, i.e., program comprehension, software evolution analysis, and software design quality assessment. For each of the contexts, we describe the visualization techniques we employ to encode the contextual data in the visualization and we illustrate the application by means of case studies. The insights gained in the three analysis contexts are complementary to each other, leading to an increasingly more complete "big picture" of the systems.We then demonstrate how the visualizations built on top of our city metaphor effectively and efficiently support reverse engineering activities, by means of an extensive controlled experiment. The design of our experiment is based on a list of desiderata that we extracted from our survey of the current body of research. We conducted the experiment over a period of six months, in four sites located in three countries, with a heterogeneous sample of subjects composed of fair shares of both academics and industry practitioners. The main result of ...

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Do Dynamic Object Process Graphs Support Program Understanding? - A Controlled Experiment.

Abstract: Abstract

Cited by 34 publications

References 7 publications

A Controlled Experiment for Program Comprehension through Trace Visualization

A Controlled Experiment for Program Comprehension through Trace Visualization

Exploiting Dynamic Information in IDEs Improves Speed and Correctness of Software Maintenance Tasks

Software systems as cities

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