Implications of integrating test-driven development into CS1/CS2 curricula

Desai, Chetan; Janzen, David S.; Clements, John

doi:10.1145/1539024.1508921

Cited by 29 publications

(14 citation statements)

References 10 publications

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“…2 the values next to each study under the column "Point" show the Hedges g summary effect size if the respective study is excluded from the synthesis process. Comparing the values in this column with the overall value in the last row, it can be seen that the [50] and Gupta and Jalote E1 [47]. Therefore, the analysis is most sensitive to, or is most effected by, the inclusion of these two experiments.…”

Section: Standardized Analysismentioning

confidence: 98%

The Effects of Test-Driven Development on External Quality and Productivity: A Meta-Analysis

Rafique

Mišić

2013

IIEEE Trans. Software Eng.

111

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This paper provides a systematic meta-analysis of 27 studies that investigate the impact of Test-Driven Development (TDD) on external code quality and productivity. The results indicate that, in general, TDD has a small positive effect on quality but little to no discernible effect on productivity. However, subgroup analysis has found both the quality improvement and the productivity drop to be much larger in industrial studies in comparison with academic studies. A larger drop of productivity was found in studies where the difference in test effort between the TDD and the control group's process was significant. A larger improvement in quality was also found in the academic studies when the difference in test effort is substantial; however, no conclusion could be derived regarding the industrial studies due to the lack of data. Finally, the influence of developer experience and task size as moderator variables was investigated, and a statistically significant positive correlation was found between task size and the magnitude of the improvement in quality.

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Section: Standardized Analysismentioning

confidence: 98%

The Effects of Test-Driven Development on External Quality and Productivity: A Meta-Analysis

Rafique

Mišić

2013

IIEEE Trans. Software Eng.

111

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“…Furthermore, they evaluated the effect of grading the test cases by comparing students whose test cases were graded to those students whose test cases were not graded. As the programming projects increased in difficulty, the code coverage decreased both for the graded test group and for the ungraded test group, although the graded test group did obtain more coverage (65% vs. ~30% on the most difficult project) [7]. In an introductory programming course, Marrero and Settle used TDD by requiring students to submit tests before submitting code.…”

Section: Approach 4: Students Learn To Test Through Use Of Test-drivementioning

confidence: 99%

Evaluating the testing ability of senior-level computer science students

Carver

Kraft

2011

2011 24th IEEE-CS Conference on Software Engineering Education and Training (CSEE&T)

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Testing is a key skill for computer science students to acquire during their studies. To determine how well students are learning this skill, we conducted an empirical study in two offerings of a senior-level computer science course. The goal of the study was to determine whether students would be able to create a small, complete test suite for a simple program. The students created a test suite first without the aid of a coverage tool and then with the aid of a coverage tool. The results indicate that without a coverage tool, students achieved significantly less than 100% statement, branch or condition coverage. When provided with a code coverage tool, students increased coverage levels. Still, examination of the test suites indicated that they were significantly larger than the minimum required. These results indicate that students cannot conduct adequate testing of even a small program. To provide context for our results, we provide a literature survey summarizing various techniques proposed for teaching testing in the computer science curriculum. We discuss each technique, its strengths, and its weaknesses.

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“…Because of the necessity of testing, more educators are including software testing [1,2] in programming and software engineering courses [3,4]. Current classroom assessment systems (e.g.,Web-CAT, ASSYST, Marmoset) use code coverage to evaluate how well students test their own code.…”

Section: Introductionmentioning

confidence: 99%

Toward practical mutation analysis for evaluating the quality of student-written software tests

Shams

Edwards

2013

Proceedings of the Ninth Annual International ACM Conference on International Computing Education Research

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Software testing is being added to programming courses at many schools, but current assessment techniques for evaluating student-written tests are imperfect. Code coverage measures are typically used in practice, but they have limitations and sometimes overestimate the true quality of tests. Others have proposed using mutation analysis instead, but mutation analysis poses a number of practical obstacles to classroom use. This paper describes a new approach to mutation analysis of studentwritten tests that is more practical for educational use, especially in an automated grading context. This approach combines several techniques to produce a novel solution that addresses the shortcomings raised by more traditional mutation analysis. An evaluation of this approach in the context of both CS1 and CS2 courses illustrates how it differs from code coverage analysis. At the same time, however, the evaluation results also raise questions of concern for CS educators regarding the relative value of more comprehensive assessment of test quality, the value of more open-ended assignments that offer significant design freedom for students, the cost of providing higher-quality reference solutions in order to support better quality assessment, and the cost of supporting assignments that require more intensive testing, such as GUI assignments.

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Implications of integrating test-driven development into CS1/CS2 curricula

Cited by 29 publications

References 10 publications

The Effects of Test-Driven Development on External Quality and Productivity: A Meta-Analysis

The Effects of Test-Driven Development on External Quality and Productivity: A Meta-Analysis

Evaluating the testing ability of senior-level computer science students

Toward practical mutation analysis for evaluating the quality of student-written software tests

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