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Maintaining software once implemented on the end-user side is laborious and, over its lifetime, is most often considerably more expensive than the initial software development. The prediction of software maintainability has emerged as an important research topic to address industry expectations for reducing costs, in particular, maintenance costs. Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning (ML) for better prediction of software maintainability. This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction (SPMP) using ML techniques. This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria: maintainability prediction techniques, validation methods, accuracy criteria, overall accuracy of ML techniques, and the techniques offering the best performance. The review process followed the well-known systematic review process. The results show that ML techniques are frequently used in predicting maintainability. In particular, artificial neural network (ANN), support vector machine/regression (SVM/R), regression & decision trees (DT), and fuzzy & neuro fuzzy (FNF) techniques are more accurate in terms of PRED and MMRE. The N-fold and leave-one-out cross-validation methods, and the MMRE and PRED accuracy criteria are frequently used in empirical studies. In general, ML techniques outperformed non-machine learning techniques, e.g., regression analysis (RA) techniques, while FNF outperformed SVM/R, DT, and ANN in most experiments. However, while many techniques were reported superior, no specific one can be identified as the best.

As the complexity of software increases, the choice of the appropriate software architecture becomes a critical task. This paper provides a guideline for selecting the appropriate software architecture based on pertinent ISO 25010 quality characteristics. The guideline was established through an analytical survey of 113 papers published from 2010 to 2014. Through this survey, we first identified a set of commonly used software architectures in the software engineering literature. Secondly, we applied the Formal Concept Analysis technique to classify each one of these architectures according to ISO 25010 quality characteristics. Finally, we identified the relationships among ISO 25010 quality characteristics, which in turn helped us to develop a guideline on how to select the appropriate software architecture with respect to ISO 25010 quality characteristics. In order to make sure about the validation of the proposed guideline, a survey with industrial experts is in progress. Data were collected from two companies working in the software development field (ST2i and Telnet).

Recognition of injured mitochondria for degradation by macroautophagy is essential for cellular health, but the mechanisms remain poorly understood. Cardiolipin is an inner mitochondrial membrane phospholipid. We found that rotenone, staurosporine, 6-hydroxydopamine and other pro-mitophagy stimuli caused externalization of cardiolipin to the mitochondrial surface in primary cortical neurons and SH-SY5Y cells. RNAi knockdown of cardiolipin synthase or of phospholipid scramblase-3, which transports cardiolipin to the outer mitochondrial membrane, decreased the delivery of mitochondria to autophagosomes. Furthermore, we found that the autophagy protein microtubule-associated-protein-1 light chain 3 (LC3), which mediates both autophagosome formation and cargo recognition, contains cardiolipin-binding sites important for the engulfment of mitochondria by the autophagic system. Mutation of LC3 residues predicted as cardiolipin-interaction sites by computational modelling inhibited its participation in mitophagy. These data indicate that redistribution of cardiolipin serves as an 'eat-me' signal for the elimination of damaged mitochondria from neuronal cells.