Comprehending reality - practical barriers to industrial adoption of software maintenance automation

Spalting fungal pigments have shown potential in technologies ranging from green energy generation to natural colorants. However, their unknown toxicity has been a barrier to industrial adoption. In order to gain an understanding of the safety of the pigments, zebrafish embryos were exposed to multiple forms of liquid media and solvent-extracted pigments with concentrations of purified pigment ranging from 0 to 50 mM from Chlorociboria aeruginosa, Chlorociboria aeruginascens, and Scytalidium cuboideum. Purified xylindein from Chlorociboria sp. did not show toxicity at any tested concentration, while the red pigment dramada from S. cuboideum was only associated with significant toxicity above 23.2 uM. However, liquid cultures and pigment extracted into dichloromethane (DCM) showed toxicity, suggesting the co-production of bioactive secondary metabolites. Future research on purification and the bioavailability of the red dramada pigment will be important to identify appropriate use; however, purified forms of the blue-green pigment xylindein are likely safe for use across industries. This opens the door to the adoption of green technologies based on these pigments, with potential to replace synthetic colorants and less stable natural pigments.

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Making the Sourcing Decision of Software Maintenance and Information Technology

IEEE Access ◽

10.1109/access.2021.3051023 ◽

2021 ◽

Vol 9 ◽

pp. 11492-11510

Author(s):

Hanif Ur Rahman ◽

Mushtaq Raza ◽

Palwasha Afsar ◽

Mukhtaj Khan ◽

Nadeem Iqbal ◽

...

Keyword(s):

Information Technology ◽

Software Maintenance

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Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽

10.3390/e23010018 ◽

2020 ◽

Vol 23 (1) ◽

pp. 18

Author(s):

Pantelis Linardatos ◽

Vasilis Papastefanopoulos ◽

Sotiris Kotsiantis

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Black Box ◽

Learning Systems ◽

Model Complexity ◽

Learning Models ◽

New Methods ◽

Industrial Adoption ◽

Machine Learning Models ◽

The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

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Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics

Technologies ◽

10.3390/technologies9010003 ◽

2020 ◽

Vol 9 (1) ◽

pp. 3

Author(s):

Gábor Antal ◽

Zoltán Tóth ◽

Péter Hegedűs ◽

Rudolf Ferenc

Keyword(s):

Software Maintenance ◽

Positive Impact ◽

Source Code ◽

Code Analysis ◽

Static Source ◽

Static Code Analysis ◽

Function Calls ◽

Hybrid Code ◽

Code Metrics ◽

Scripting Language

Bug prediction aims at finding source code elements in a software system that are likely to contain defects. Being aware of the most error-prone parts of the program, one can efficiently allocate the limited amount of testing and code review resources. Therefore, bug prediction can support software maintenance and evolution to a great extent. In this paper, we propose a function level JavaScript bug prediction model based on static source code metrics with the addition of a hybrid (static and dynamic) code analysis based metric of the number of incoming and outgoing function calls (HNII and HNOI). Our motivation for this is that JavaScript is a highly dynamic scripting language for which static code analysis might be very imprecise; therefore, using a purely static source code features for bug prediction might not be enough. Based on a study where we extracted 824 buggy and 1943 non-buggy functions from the publicly available BugsJS dataset for the ESLint JavaScript project, we can confirm the positive impact of hybrid code metrics on the prediction performance of the ML models. Depending on the ML algorithm, applied hyper-parameters, and target measures we consider, hybrid invocation metrics bring a 2–10% increase in model performances (i.e., precision, recall, F-measure). Interestingly, replacing static NOI and NII metrics with their hybrid counterparts HNOI and HNII in itself improves model performances; however, using them all together yields the best results.

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