iALBMAD: an improved agile-based layered approach for mobile app development

The demand to acquire improved efficiency, agility, and adaptability led to rapid evolution in mobile app development (MAD). Agile approaches are recognized for being cooperative and iterative, but there are still issues in handling a range of MAD necessities. The objective here is to blend the best practices of several prominent agile approaches and non-agile approaches to form an innovative and improved MAD approach, which we refer to as the improved Agile and Lean-based MAD Approach (iALBMAD), and this approach was the improved upon our previous work, ALBMAD. Here, three aspects of improvement concerning the discovery of suitable app attributes and best practices at various MAD activities and strengthening requirement gathering activities are exploited. For this to be accomplished, first we determined different app attributes that affect the MAD, agile and non-agile best practices, and machine learning (ML) functioning in MAD from the accessible literature. Now, we have equipped ALBMAD with all these gained aspects as per their applicability and offered it to 18 MAD experts to obtain suggestions for its improvement. Considering the experts’ opinions, a three-layered approach, namely, iALBMAD, was developed. In iALBMAD, automation and an iterative cycle are established to meet finished needs; these revisions may boost the quality of requirements and minimize time. Specific and experts validated best practices and app attributes suitable for each activity of iALBMAD are offered, which will assist less-skilled developers. Thirteen users verified the usability of six teams’ apps created using three different approaches, and the results show that the iALBMAD performs better than other approaches. The suggested approach and the discoveries will provide insightful information for individuals and MAD firms aiming to improve the way of MAD.