Analysis and Maintainability of Complex Industry Test Code Using Clone Detection
Bibliographic record
Abstract
Many companies, including Ericsson, experience increased software verification costs.Agile cross-functional teams find it easy to make new additions of test cases for every change and fix.The consequence of this phenomenon is the duplications of test code [2], which is often referred to as (test) code clone.In this thesis, we attempt to understand the prevalence of test code clones, thus contributing to the clone detection and software testing fields, and we then study how test code can be refactored to remove clones, therefore improving software testing activities.In this thesis, we aim to achieve the following goals.Firstly, we aim to detect clones in industry test codes.We found, in the subjects shared by our industry partner, that 49% of lines of code of the entire C test code are clones, which is also equivalent to 36% of all the test cases; We found that 73% of lines of code of the entire Java test code is a clone, amounting to 94% of all the test cases.The results we report on in our study include figures about clone frequencies, types, similarity, fragments, size distributions, and the number of line differences in cloned test cases.Secondly, following standard definitions of clone types, if the source code contains fragments that are in a Type-1 clone class, we theoretical argue and empirically confirm that those fragments also belong to a Type-2 clone class; Further, assuming the source code includes fragments that belong to a Type-2 consistent clone class, these fragments will also appear in a Type-2 blind clone class; Such inclusions also occur with Type-3 clone classes, with different threshold values to detect differences.When one relies on standard definitions of source code clone types, they are therefore running a risk of overlapping information being returned for different types of clone classes.Further, if one interprets Labiche, for his valuable advice, guidance, support, and various ideas.His great ideas have guided me to conduct my research properly.This thesis would not have completed without him.I would also like to thank Dr. Sigrid Eldh from
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".