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Register a new userMandating Code Disclosure is Unnecessary -- Strict Model Verification Does Not Require Accessing Original Computer Code
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Added by
Sasanka Sekhar Chanda
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Authors
Sasanka Sekhar Chanda
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Mandating public availability of computer code underlying computational simulation modeling research ends up doing a disservice to the cause of model verification when inconsistencies between the specifications in the publication text and specifications in the computer code go unchallenged. Conversely, a model is verified when an independent researcher undertakes the set of mental processing tasks necessary to convert natural language specifications in a publication text into computer code instructions that produce numerical or graphical outputs identical to the outputs found in the original publication. The effort towards obtaining convergence with the numerical or graphical outputs directs intensive consideration of the publication text. The original computer code has little role to play in determining the verification status - verified/ failed verification. An insight is obtained that skillful deployment of human intelligence is feasible when effort-directing feedback processes are in place to appropriately go around the human frailty of giving up in the absence of actionable feedback. This principle can be put to use to develop better organizational configurations in business, government and society.
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Static code analysis tools and integrated development environments present developers with quality-related software metrics, some of which describe the understandability of source code. Software metrics influence overarching strategic decisions that impact the future of companies and the prioritization of everyday software development tasks. Several software metrics, however, lack in validation: we just choose to trust that they reflect what they are supposed to measure. Some of them were even shown to not measure the quality aspects they intend to measure. Yet, they influence us through biases in our cognitive-driven actions. In particular, they might anchor us in our decisions. Whether the anchoring effect exists with software metrics has not been studied yet. We conducted a randomized and double-blind experiment to investigate the extent to which a displayed metric value for source code comprehensibility anchors developers in their subjective rating of source code comprehensibility, whether performance is affected by the anchoring effect when working on comprehension tasks, and which individual characteristics might play a role in the anchoring effect. We found that the displayed value of a comprehensibility metric has a significant and large anchoring effect on a developers code comprehensibility rating. The effect does not seem to affect the time or correctness when working on comprehension questions related to the code snippets under study. Since the anchoring effect is one of the most robust cognitive biases, and we have limited understanding of the consequences of the demonstrated manipulation of developers by non-validated metrics, we call for an increased awareness of the responsibility in code quality reporting and for corresponding tools to be based on scientific evidence.
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