A Metric for Questions and Discussions Identifying Concerns in Software Reviews
Abstract
:1. Introduction
2. Software Reviews
3. Effective Questions in Reviews
3.1. Definition
3.2. Defect Category and Effective Questions in Review Process
- -
- Identifying
- -
- Sharing
- -
- Recording
3.3. Literature Review
4. Case Study
4.1. Goal
4.2. Projects
4.3. Metrics and Procedure
4.4. Results
5. Discussion
5.1. RQ: Does the Number of Effective Questions in a Review Affect the Quality of Subsequent Testing?
5.2. Implications for Practitioners
5.3. Threats to Validity
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
References
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Categories | Text Referring the Defect Categories in the Previous Studies |
---|---|
Did ∩ Dst | To support decision making, discussants can also vote by rating any potential defect as true defect [44]. Collated defects: the number of defects merged from individual findings to be discussed during the meeting. True defects: the number of defects for which consensus was reached during the meeting in considering them as true defects [45]. We used the information from the repair form and interviews with the author to classify each issue as a true defect (if the author was required to make an execution affecting change to resolve it) [46]. |
Did ∩ Dsf | False positives are items reported by subjects as defects, when in fact no defect exists [47]. |
Did ∩ (Dst ∪ Dsf) | In addition to the instructions from the preparation phase, the instructions in the meeting phase were: use the individual inspection record and decide which are faults and which are false positives [48]. |
Dst | True defects: the number of defects for which consensus was reached during the meeting in considering them as true defects [45]. |
Dsf | Removed false positives: the number of defects for which consensus was reached during the meeting in considering them as not true defects, thus as false positives [45]. |
Dst ∩ Drf | In the Discrimination stage, discussion takes place asynchronously as in a discussion forum. When a consensus has been reached, the moderator can mark potential defects as false positives, thus removing them from the list that will go to the author for rework (potential defects marked as false positives appear strikethrough in Figure 4) [44]. |
Drf | False positives were issues that were identified in the meeting but that were discovered not to be defects either during the meeting or after. The decision whether a defect was a false positive was done by the code review team [39]. |
Article | Definitions of False-Positive Defects | Percentages of False-Positive Defects | Definitions of True Defects |
---|---|---|---|
[9] | False positives (issues raised as defects that are not actual defects) False positives, the number of invalid defects recorded by the group | 22% | Defects, the total number of distinct, valid defects detected by a group |
[36] | False positives (issues raised as defects that are not actual defects) | 22% | Actual defects |
[39] | False positives were issues that were identified in the meeting but that were discovered not to be defects either during the meeting or after | 22% | If the code review team finds an issue and agrees that it is a deviation from quality, the issue is counted as a defect |
[42] | False positives (no real usability problems) | 43.10% | Real usability problem |
[43] | False positives (reported defects that were not considered to be actual defects) | - | Actual defects |
[44] | False positives (non-true defects) False positives (defects erroneously reported as such by inspectors) | 46% | True defects |
[45] | For which consensus was reached during the meeting in considering them as not true defects, thus as false positives | - | True defects: the number of defects for which consensus was reached during the meeting in considering them as true defects |
[46] | False positive (any issue which required no action) | 20% | True defect (if the author was required to make an execution affecting change to resolve it), soft maintenance issue (any other issue which the author fixed) |
[47] | False positives are items reported by subjects as defects, when in fact no defect exists | - | Defects |
[49] | A false positive is a description which is not a true defect, i.e., does not require rework | - | A true defect is a description of a positively identified defect which requires rework; it causes the program to fail, and violates the given specifications and design |
[50] | False positives (erroneously identified defects) False positives are the non-true defects—defects that require no repair | 42.62% | True defects |
[51] | It classifies too many consistent designs as inconsistent (false positives) | - | True positive |
[52] | False positive (FA)—defects that do not exist but were wrongly identified | - | True defects (TR)—defects that actually exist and have been successfully detected |
[53] | False defect estimations, known as false positive | - | The number of true defect estimations, known as true positive |
[54] | False positive rate: the percentage of issues reported by an inspector that turn out not to represent real quality problems in the artifact | 80% | Defect detection rate: the percentage of known defects in a given software artifact that are found during the inspection |
[55] | False positives (not identified from preparation) | - | True defects Net defects |
[56] | A false positive—an obviously wrong statement of the document. | - | True defect |
[57] | False positive—items pointed by the subjects that do not correspond to a defect of the RD RD: the Requirements Document | - | Defects—items that really are defects of the RD RD: the Requirements Document |
Name | Description | |
---|---|---|
Lines of code | New (nLOC) | Lines of code newly developed, excluding headers and comments |
Changed (cLOC) | Lines of code changed from the code base or reused source code, excluding headers and comments | |
Reused (rLOC) | Lines of code reused from the code base or another product, excluding headers and comments | |
Product size (SZ) | Product size for assessing development effort consumption in the project management defined by the standard development process. SZ = nLOC + cLOC + rLOC × coefficient (where the coefficient is determined by the project attributes) | |
Number of defects and questions in reviewed | True defects (rNOD) | Sum of the number of defects detected in software architecture design, software detailed design, and code reviews |
Effective questions categorized as false-positive defects (NOQf) | Sum of the number of effective questions subsequently categorized as false-positive defects detected in software architecture design, software detailed design, and code reviews | |
Number of defects detected in the test (tNOD) | Sum of numbers of defects detected in the unit test, software integration test, and software qualification test |
Name | Description | |
---|---|---|
Q | Proportion of the number of defects detected in testing to the product size | tNOD/SZ |
p1 | Proportion of the reused lines of code to the lines of code | rLOC/(nLOC + cLOC + rLOC) |
p2 | Proportion of the number of true defects to the product size | rNOD/SZ |
p3 | Proportion of the number of effective questions categorized as false-positive defects to the product size | NOQf/SZ |
p4 | Proportion of the number of effective questions categorized as false-positive defects to the sum of the number of defects and effective questions categorized as false-positive defects | NOQf/(rNOD + NOQf) |
SZ | rNOD | NOQf | tNOD | |
---|---|---|---|---|
max | 110,5000 | 1871 | 384 | 711 |
min | 1330 | 32 | 0 | 3 |
median | 144,390 | 589 | 99 | 171 |
Q | p1 | p2 | p3 | p4 | |
---|---|---|---|---|---|
max | 6.50 | 0.97 | 46.29 | 7.90 | 0.29 |
min | 0.54 | 0.00 | 0.55 | 0.00 | 0.00 |
median | 2.61 | 0.75 | 12.03 | 1.42 | 0.17 |
Estimate (b) | Std. Error | t Value | Pr (>|t|) | VIF | |
---|---|---|---|---|---|
p1 | 2.53 | 0.88 | 2.89 | 0.01 | 1.07 |
p3 | 0.42 | 0.15 | 2.91 | 0.01 | 1.38 |
p4 | −9.68 | 3.69 | −2.63 | 0.02 | 1.46 |
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Wakimoto, M.; Morisaki, S. A Metric for Questions and Discussions Identifying Concerns in Software Reviews. Software 2022, 1, 364-380. https://doi.org/10.3390/software1030016
Wakimoto M, Morisaki S. A Metric for Questions and Discussions Identifying Concerns in Software Reviews. Software. 2022; 1(3):364-380. https://doi.org/10.3390/software1030016
Chicago/Turabian StyleWakimoto, Michiyo, and Shuji Morisaki. 2022. "A Metric for Questions and Discussions Identifying Concerns in Software Reviews" Software 1, no. 3: 364-380. https://doi.org/10.3390/software1030016
APA StyleWakimoto, M., & Morisaki, S. (2022). A Metric for Questions and Discussions Identifying Concerns in Software Reviews. Software, 1(3), 364-380. https://doi.org/10.3390/software1030016