Evolutionary Search Techniques with Strong Heuristics for Multi-Objective Feature Selection in Software Product Lines

Software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature models) using various search-based software engineering methods. Our main result is that as we increase the number of optimization objectives, the methods in widespread use (e.g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-Based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0 violations of domain constraints. We also present significant improvements to IBEA\u27s performance by employing three strong heuristic techniques that we call PUSH, PULL, and seeding. The PUSH technique forces the evolutionary search to respect certain rules and dependencies defined by the feature models, while the PULL technique gives higher weight to constraint satisfaction as an optimization objective and thus achieves a higher percentage of fully-compliant configurations within shorter runtimes. The seeding technique helps in guiding very large feature models to correct configurations very early in the optimization process. Our conclusion is that the methods we apply in search-based software engineering need to be carefully chosen, particularly when studying complex decision spaces with many optimization objectives. Also, we conclude that search methods must be customized to fit the problem at hand. Specifically, the evolutionary search must respect domain constraints

Data-Driven Search-based Software Engineering

This paper introduces Data-Driven Search-based Software Engineering (DSE), which combines insights from Mining Software Repositories (MSR) and Search-based Software Engineering (SBSE). While MSR formulates software engineering problems as data mining problems, SBSE reformulate Software Engineering (SE) problems as optimization problems and use meta-heuristic algorithms to solve them. Both MSR and SBSE share the common goal of providing insights to improve software engineering. The algorithms used in these two areas also have intrinsic relationships. We, therefore, argue that combining these two fields is useful for situations (a) which require learning from a large data source or (b) when optimizers need to know the lay of the land to find better solutions, faster. This paper aims to answer the following three questions: (1) What are the various topics addressed by DSE?, (2) What types of data are used by the researchers in this area?, and (3) What research approaches do researchers use? The paper briefly sets out to act as a practical guide to develop new DSE techniques and also to serve as a teaching resource. This paper also presents a resource (tiny.cc/data-se) for exploring DSE. The resource contains 89 artifacts which are related to DSE, divided into 13 groups such as requirements engineering, software product lines, software processes. All the materials in this repository have been used in recent software engineering papers; i.e., for all this material, there exist baseline results against which researchers can comparatively assess their new ideas

Ameliorative and protective effects of ginger and its main constituents against natural, chemical and radiation-induced toxicities: A comprehensive review

Fatal unintentional poisoning is widespread upon human exposure to toxic agents such as pesticides, heavy metals, environmental pollutants, bacterial and fungal toxins or even some medications and cosmetic products. In this regards, the application of the natural dietary agents as antidotes has engrossed a substantial attention. One of the ancient known traditional medicines and spices with an arsenal of metabolites of several reported health benefits is ginger. This extended literature review serves to demonstrate the protective effects and mechanisms of ginger and its phytochemicals against natural, chemical and radiation-induced toxicities. Collected data obtained from the in-vivo and in-vitro experimental studies in this overview detail the designation of the protective effects to ginger\u27s antioxidant, anti-inflammatory, and anti-apoptotic properties. Ginger\u27s armoury of phytochemicals exerted its protective function via different mechanisms and cell signalling pathways, including Nrf2/ARE, MAPK, NF-ƙB, Wnt/β-catenin, TGF-β1/Smad3, and ERK/CREB. The outcomes of this review could encourage further clinical trials of ginger applications in radiotherapy and chemotherapy regime for cancer treatments or its implementation to counteract the chemical toxicity induced by industrial pollutants, alcohol, smoking or administered drugs