Test-to-code traceability links model the relationships between test artefacts and code artefacts. When utilised during the development process, these links help developers to keep test code in sync with tested code, reducing the rate of test failures and missed faults. Test-to-code traceability links can also help developers to maintain an accurate mental model of the system, reducing the risk of architectural degradation when making changes. However, establishing and maintaining these links manually places an extra burden on developers and is error-prone. This paper presents TCtracer, an approach and implementation for the automatic establishment of test-to-code traceability links. Unlike existing work, TCtracer operates at both the method level and the class level, allowing us to establish links between tests and functions, as well as between test classes and tested classes. We improve over existing techniques by combining an ensemble of new and existing techniques and exploiting a synergistic flow of information between the method and class levels. An evaluation of TCtracer using four large, wellstudied open source systems demonstrates that, on average, we can establish test-to-function links with a mean average precision (MAP) of 78% and test-class-to-class links with an MAP of 93%.
Software developers all over the world use Stack Overflow (SO) to interact and exchange code snippets. Research also uses SO to harvest code snippets for use with recommendation systems. However, previous work has shown that code on SO may have quality issues, such as security or license problems. We analyse Python code on SO to determine its coding style compliance. From 1,962,535 code snippets tagged with 'python', we extracted 407,097 snippets of at least 6 statements of Python code. Surprisingly, 93.87% of the extracted snippets contain style violations, with an average of 0.7 violations per statement and a huge number of snippets with a considerably higher ratio. Researchers and developers should, therefore, be aware that code snippets on SO may not representative of good coding style. Furthermore, while user reputation seems to be unrelated to coding style compliance, for posts with vote scores in the range between-10 and 20, we found a strong correlation (r = −0.87, p < 10 −7) between the vote score a post received and the average number of violations per statement for snippets in such posts.
Test generation can have a large impact on the software engineering process by decreasing the amount of time and effort required to maintain a high level of test coverage. This increases the quality of the resultant software while decreasing the associated effort. In this paper, we present TestNMT, an experimental approach to test generation using neural machine translation. TestNMT aims to learn to translate from functions to tests, allowing a developer to generate an approximate test for a given function, which can then be adapted to produce the final desired test. We also present a preliminary quantitative and qualitative evaluation of TestNMT in both cross-project and within-project scenarios. This evaluation shows that TestNMT is potentially useful in the within-project scenario, where it achieves a maximum BLEU score of 21.2, a maximum ROUGE-L score of 38.67, and is shown to be capable of generating approximate tests that are easy to adapt to working tests. CCS CONCEPTS • Computing methodologies → Machine translation; • Software and its engineering → Software testing and debugging.
The reuse of artefacts is fundamental to software development and can reduce development cost and time as well as improve the quality of the output. For example, developers often create new tests from existing tests by copying and adapting them. However, reuse opportunities are often missed due to the cost of discovering suitable artefacts to reuse.Development artefacts form groups that have both internal connections between artefacts of the same type, and cross-group connections between artefacts of different types. When a pair of artefact groups are considered, the cross-group connections form a bipartite graph. This paper presents RASHID, an abstract framework to assist artefact reuse by predicting edges in these bipartite graphs. We instantiate RASHID with RELATEST, an approach to assist developers to reuse tests. RELATEST recommends existing tests that are closely related to a new function and can, therefore, be easily adapted to test the new function. Our evaluation finds that RELATEST's recommendations result in an average 58% reduction in developer effort (measured in tokens), for 75% of functions, resulting in an overall saving of 43% of the effort required to create tests. A user study revealed that, on average, developers needed 10 minutes less to develop a test when given RELATEST recommendations and all developers reported that the recommendations were useful.
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