# SPL Features Quantification and Selection Based on Multiple Multi-Level Objectives
## 2 Literature Review
They suggested as future work that they have not dealt with a large number of a feature on the configuration of stage-2. In addition, the synthetic SPL (with no real asset) was used and the approach was not tested on a large number of features. [PERSON] et al. (2014) proposed H-H (Hyper Heuristics) approach to select dynamically the best operator in SPL for testing, as the increasing size and complexity of application can make product line testing impossible. They considered four objectives to the problem, which include product quantity, mutation score, pair wise coverage and dissimilarity of products. A hyper heuristic is automatic selection or generation of heuristics for solving a complex problem. The hyper heuristic model was used with 4 MOEAS namely NSGA 2, IBEA, SPEA2, and MOEA/D-DRA incorporating 2 selection methods, random and Upper Confidence Bound (UCB). It was found that NSGA 2-H-H was better and dominant over NSGA 2 for large instances, while for small instances random selection was acceptable. [PERSON] et al. (2015) proposed a new algorithm by the name of IVEA-2 to solve the problem of product customization in SPL with multiple objectives. Five objectives in this case study were considered (correctness, reuse, less error, lower cost, and more features) so that an optimal solution between competing and conflicting objectives is taken into consideration. They used the same dataset used by [PERSON] et al. (2011). The results were tested against IBEA and SATIBEA algorithms and the results show that IVEA-2 outperforms on the effectiveness of producing the optimal valid solutions for large-scale feature models over a set period of time, having better diversity and convergence. Table 2 summarizes the MOO algorithms and their effectiveness based on different sets of objectives. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \# & **Paper** & \
\begin{tabular}{c} **\# of Objectives** \\ **Tested** \\ \end{tabular} & **Algorithm Used** &
\begin{tabular}{c} **Algorithms Used for** \\ **Comparison/Validation** \\ \end{tabular} & **Results** \\ \hline \multirow{4}{*}{1} & Optimum feature selection in software & & & & & IBEA in searching complex structures \\  & product lines: Let your model and values guide & & & & SPEA-2 & with many objectives has a clear advantage over others. \\ \hline \multirow{4}{*}{2} & Combining & & & & & over others. \\  & Multi-Objective Search & & & & & \\  & and Constraint Solving for Configuring Large & & & & & \\  & Software Product Lines & & & & & \\ \hline \multirow{4}{*}{3} & Optimized feature selection towards & & & & & IVEA has better performance in four factors Spread, HVD, IGD and EPSLILON \\  & functional and nonfunctional requirements in software & & & & IGEA & Correctness and \\  & product lines. & & & & & \\ \hline \multirow{4}{*}{4} & IBED: Combining IBEA and DE for optimal feature selection in SPLE & & & IBEA & Correctness and \\  & & & & & NSGA 2 & of the found solutions. Reduced the time in finding a solution by 30–40 s. \\ \hline \multirow{4}{*}{5} & Binary Multi-objective PSO and GA for Adding New Features into an Existing Product Line & & & & Adding new features into the existing product line. \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of MOO (Multi-Objective Optimization) Algorithms. This section presented a state-of-the-art literary review on feature quantification and selection algorithms. The current approaches do not support feature selection based on multiple multi-level objectives. In the proposed approach, objectives can be set at product/category and property level. Also, they do not propose an enriched quantifying text-based feature properties. The following section introduces our work.