Cook up better code [Software code optimisation]

2007 ◽  
Vol 5 (6) ◽  
pp. 24-27
Author(s):  
J.-E. Dahlin
Keyword(s):  
Software Code

Agent Mediated Code Comprehension: A Cognitive Challenge

2019 ◽  
Vol 11 (2) ◽  
Author(s):  
Ram Gopal Gupta ◽  
Bireshwar Dass Mazumdar ◽  
Kuldeep Yadav
Keyword(s):  
Information Systems ◽  
Software Design ◽  
Business Processes ◽  
Software Agents ◽  
Cognitive Model ◽  
Cognitive Models ◽  
Software Market ◽  
Computing Paradigm ◽  
Cognitive Challenge ◽  
Software Code

The rapidly changing needs and opportunities of today’s global software market require unprecedented levels of code comprehension to integrate diverse information systems to share knowledge and collaborate among organizations. The combination of code comprehension with software agents not only provides a promising computing paradigm for efficient agent mediated code comprehension service for selection and integration of inter-organizational business processes but this combination also raises certain cognitive issues that need to be addressed. We will review some of the key cognitive models and theories of code comprehension that have emerged in software code comprehension. This paper will propose a cognitive model which will bring forth cognitive challenges, if handled properly by the organization would help in leveraging software design and dependencies.

Machine learning steered symbolic execution framework for complex software code

2021 ◽  
Author(s):  
Lei Bu ◽  
Yongjuan Liang ◽  
Zhunyi Xie ◽  
Hong Qian ◽  
Yi-Qi Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Symbolic Execution ◽  
Software Code

scholarly journals PathoFusion: An Open-Source AI Framework for Recognition of Pathomorphological Features and Mapping of Immunohistochemical Data

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 617
Author(s):  
Guoqing Bao ◽  
Xiuying Wang ◽  
Ran Xu ◽  
Christina Loh ◽  
Oreoluwa Daniel Adeyinka ◽  
...  
Keyword(s):  
Neural Network ◽  
Tumor Vasculature ◽  
Integrated System ◽  
Pathological Features ◽  
Adjacent Tissue ◽  
Tissue Sections ◽  
Contextual Feature ◽  
Hematoxylin And Eosin ◽  
Feature Information ◽  
Software Code

We have developed a platform, termed PathoFusion, which is an integrated system for marking, training, and recognition of pathological features in whole-slide tissue sections. The platform uses a bifocal convolutional neural network (BCNN) which is designed to simultaneously capture both index and contextual feature information from shorter and longer image tiles, respectively. This is analogous to how a microscopist in pathology works, identifying a cancerous morphological feature in the tissue context using first a narrow and then a wider focus, hence bifocal. Adjacent tissue sections obtained from glioblastoma cases were processed for hematoxylin and eosin (H&E) and immunohistochemical (CD276) staining. Image tiles cropped from the digitized images based on markings made by a consultant neuropathologist were used to train the BCNN. PathoFusion demonstrated its ability to recognize malignant neuropathological features autonomously and map immunohistochemical data simultaneously. Our experiments show that PathoFusion achieved areas under the curve (AUCs) of 0.985 ± 0.011 and 0.988 ± 0.001 in patch-level recognition of six typical pathomorphological features and detection of associated immunoreactivity, respectively. On this basis, the system further correlated CD276 immunoreactivity to abnormal tumor vasculature. Corresponding feature distributions and overlaps were visualized by heatmaps, permitting high-resolution qualitative as well as quantitative morphological analyses for entire histological slides. Recognition of more user-defined pathomorphological features can be added to the system and included in future tissue analyses. Integration of PathoFusion with the day-to-day service workflow of a (neuro)pathology department is a goal. The software code for PathoFusion is made publicly available.

CP-Miner: finding copy-paste and related bugs in large-scale software code

2006 ◽  
Vol 32 (3) ◽  
pp. 176-192 ◽  
Author(s):  
Z. Li ◽  
S. Lu ◽  
S. Myagmar ◽  
Y. Zhou
Keyword(s):  
Large Scale ◽  
Software Code

scholarly journals The Relation of Test-Related Factors to Software Quality: A Case Study on Apache Systems

2021 ◽  
Vol 26 (2) ◽  
Author(s):  
Fabiano Pecorelli ◽  
Fabio Palomba ◽  
Andrea De Lucia
Keyword(s):  
Software Quality ◽  
Compelling Evidence ◽  
Related Factors ◽  
Code Coverage ◽  
Production Code ◽  
Software Code ◽  
Class Test ◽  
Code Quality ◽  
Lack Of Knowledge

AbstractTesting represents a crucial activity to ensure software quality. Recent studies have shown that test-related factors (e.g., code coverage) can be reliable predictors of software code quality, as measured by post-release defects. While these studies provided initial compelling evidence on the relation between tests and post-release defects, they considered different test-related factors separately: as a consequence, there is still a lack of knowledge of whether these factors are still good predictors when considering all together. In this paper, we propose a comprehensive case study on how test-related factors relate to production code quality in Apache systems. We first investigated how the presence of tests relates to post-release defects; then, we analyzed the role played by the test-related factors previously shown as significantly related to post-release defects. The key findings of the study show that, when controlling for other metrics (e.g., size of the production class), test-related factors have a limited connection to post-release defects.

Software code base conversions

2007 ◽  
Author(s):  
Kenn R. Luecke ◽  
Brian J. Ellis ◽  
Ira Baxter ◽  
Robert L. Akers ◽  
Michael Mehlich
Keyword(s):  
Code Base ◽  
Software Code

Administrative Justice in a Digital World

2021 ◽  
Author(s):  
Paul Henman
Keyword(s):  
Computer Code ◽  
Digital Data ◽  
Administrative Discretion ◽  
Technical Standards ◽  
Organizational Governance ◽  
Digital World ◽  
Use Context ◽  
Software Code ◽  
Digital Algorithms ◽  
Administrative Justice

Using digital tools in administrative decision-making—from automation of relatively simple decisions to artificial intelligence judgements—both enhances and challenges the operation of administrative justice. By beginning with an understanding of digital algorithms as comprising computer code, digital data, and use context, this chapter highlights challenges for administrative justice in administrative discretion, data challenges, automating decisions and errors, information about administrative justice, appealability and accountability responsibility, and explainability. The chapter then examines legal, policy, and technological responses to strengthen administrative justice, including expanding digital rights, bolstering review rights via providing explanations and software code, and instituting organizational governance innovations and technical standards.

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