Code Complete
By Andrew M. Kuhn. Technometrics, 2005
383 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/b418b65ce515234eb89fdd83a4cbfde1ec6d5dd4
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Miltiadis Allamanis
- Earl T. Barr
- Charles Sutton
- Uri Alon
- Kui Liu
- Christian Bird
- Michael Pradel
- Rafael-Michael Karampatsis
Citations in the corpus
- The New C Standard (Excerpted material) An Economic and Cultural Commentary (2008, unknown): “Coding Guidelines Many coding guideline documents[14, 16] specify what they consider to be good commenting practices.”
- Automating spoken dialogue management design using machine learning: An industry perspective (2008, background): “The best practice for testing is to plan for it from the beginning of the design process as opposed to doing it as an afterthought (McConnell, 2004).”
- Model-Based Testing in Practice (2005, background): “This is even if the design of such studies is intricate, and even if, in sum, results of comparable work in the domain of error classifications are rather inconclusive [6].”
- Learning to Spot and Refactor Inconsistent Method Names (2019, background): “As highlighted by a number of industry experts, including McConnell [2], Beck [3], and Martin [4], naming is one of the key activities in programming.”
- Suggesting accurate method and class names (2015, background): “Indeed, leading industrial experts, including Beck [9], Mc-Connell [34], and Martin [33], have stressed the importance of identifier naming in software.”
- Towards a universal code formatter through machine learning (2016, background): “The way source code is formatted has a significant impact on its comprehensibility [9], and manually reformatting code is just not an option [8, p.399].”