ﻻ يوجد ملخص باللغة العربية
Background: Test-driven development (TDD) is a technique that repeats short coding cycles interleaved with testing. The developer first writes a unit test for the desired functionality, followed by the necessary production code, and refactors the code. Many empirical studies neglect unique process characteristics related to TDD iterative nature. Aim: We formulate four process characteristic: sequencing, granularity, uniformity, and refactoring effort. We investigate how these characteristics impact quality and productivity in TDD and related variations. Method: We analyzed 82 data points collected from 39 professionals, each capturing the process used while performing a specific development task. We built regression models to assess the impact of process characteristics on quality and productivity. Quality was measured by functional correctness. Result: Quality and productivity improvements were primarily positively associated with the granularity and uniformity. Sequencing, the order in which test and production code are written, had no important influence. Refactoring effort was negatively associated with both outcomes. We explain the unexpected negative correlation with quality by possible prevalence of mixed refactoring. Conclusion: The claimed benefits of TDD may not be due to its distinctive test-first dynamic, but rather due to the fact that TDD-like processes encourage fine-grained, steady steps that improve focus and flow.
Diversity has been used as an effective criteria to optimise test suites for cost-effective testing. Particularly, diversity-based (alternatively referred to as similarity-based) techniques have the benefit of being generic and applicable across diff
Testing processes and workflows in information and Internet of Things systems is a major part of the typical software testing effort. Consistent and efficient path-based test cases are desired to support these tests. Because certain parts of software
In this paper, we propose to use production executions to improve the quality of testing for certain methods of interest for developers. These methods can be methods that are not covered by the existing test suite, or methods that are poorly tested.
Growth of software size, lack of resources to perform regression testing, and failure to detect bugs faster have seen increased reliance on continuous integration and test automation. Even with greater hardware and software resources dedicated to tes
It is integral to test API functions of widely used deep learning (DL) libraries. The effectiveness of such testing requires DL specific input constraints of these API functions. Such constraints enable the generation of valid inputs, i.e., inputs th