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Does agile methodology fit all characteristics of software projects? Review and analysis

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Abstract

The agile paradigm for software projects has significantly impacted software development worldwide. It is currently widely accepted as having advantages in accommodating changes due to volatile requirements. However, several aspects of the agile paradigm and its compatibility with various software project characteristics remain empirically under-researched. In this paper, we employ a systematic literature review (SLR) to assess the compatibility of agile methodology with the characteristics of software development projects. We have mapped the characteristics to create a two-dimensional decision-making framework comprised of the software development life cycle (SDLC) phases (the y-axis) and knowledge areas derived from the Project Management Body of Knowledge (PMBOK) (the x-axis). We have then explored the position-sentiment regarding each cell of the decision-making framework as it is expressed in a wide set of academic articles, to help researchers and practitioners evaluate the compatibility of the agile methodology with the software project they are dealing with. Predictably, this would assist them in effectively assigning the agile methodology to suitable projects.

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Authors and Affiliations

  1. Information Systems Program, Graduate School of Business Administration, Bar Ilan University, Ramat-Gan, 5900, Israel

    David Itzik & Gelbard Roy

Authors
  1. David Itzik
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  2. Gelbard Roy
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Correspondence to Gelbard Roy.

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Communicated by: Daniel Méndez

Appendices

Appendix 1: SLR - Primary Studies

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Appendix 2

Table 4 Syntax Used for Selection from Electronic Databases
Full size table

Appendix 3

Table 5 Overview of the Primary Studies
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Appendix 4

Table 6 Studies’ Mapping into Decision-Framework based on Sentiment Score
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Appendix 5: Description of Calculations in the Decision-Framework Processing

Figures 10, 11 and 12 present three forms of the decision framework as resulted through the data processing steps. The calculations method are also detailed described.

Fig. 10
figure 10

Decision-Making Framework – Number of studies

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Fig. 11
figure 11

Decision-Making Framework – Sentiment Score

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The calculation method:

  • Let S denotes the primary studies included in this review, namely S = {S1, S2, … S126}.

  • Let P denotes the SLDC phases included in the decision framework, namely P = {Architecture and Design, Development, Quality Assurance, Maintenance, SDLC General}.

  • Let C denotes projects characteristics include in the decision framework, namely, C = {Quality, Complexity, Requirements, Schedule, Budget, Risk, Productivity, Communication, Distribution, Decision-making, Low-Skills, High-Skills, Small-Medium Scale, Large Scale}.

  • Let (P,C) denote cell position within the decision framework.

  • Let POL(s,p,c) denote the sum sentiment score for a study in S associated with the cell (p,c).

First, we generated a decision-making framework with the number of distinct studies for each cell. The calculation for each cell is as follows:

$$\textrm{Number}\ \textrm{of}\ {\textrm{studies}}_{\left(\textrm{P},\textrm{C}\right)}=\mid \left\{\textrm{S}1,\textrm{S}2,\dots, \textrm{S}126\right\}\mid$$

Next, we generated a decision-making framework with the total sentiment score for each cell. Since each study featured a different number of sentiments, studies with a higher number of associated sentiments were presumed to have a greater impact on the final score, thus creating bias. Therefore, we adopted the following procedure to ensure that each study would equally impact the final score by:

  1. 1.

    Calculating the total sentiment score for each cell within the decision-making framework for each of the 126 primary study in {S}, as follows (for the results of this step see Appendix Table 6):

    $$\textrm{POL}\left(\textrm{s},\textrm{p},\textrm{c}\right)=\sum \textrm{Sentiment}\ \textrm{Score}$$
  2. 2.

    POL(s,p,c) normalization to 1,0 and − 1 according to the following conditions:

    1. i.

      If the score total was positive (higher than 0), the score value was set at 1.

    2. ii.

      If the score total was negative (lower than 0), the score value was set at −1.

    3. iii.

      If the score total was neutral (neither positive nor negative), the score value was set at 0.

  3. 3.

    Calculating collectively the total sentiment score for each cell of the primary studies {S}, as follows:

    $$\textrm{Sum}\ \textrm{of}\ \textrm{Sentiment}\ \textrm{Score}\ \left(\textrm{p},\textrm{c}\right)=\sum\nolimits_{s=1}^{126} POL\left(\textrm{s},\textrm{p},\textrm{c}\right)$$

The results of both decision-making frameworks, number of studies, and total sentiment scores were used to generate a decision-making framework that characterizes each cell by one of the classifications presented in Table 2. Following is a description of the classification procedure:

If the number of studies is (P,C) ≤ 5 then set classification as ‘Lack of evidence’, else calculate the following:

\(Cell\ Score\ (CS)=\frac{\textrm{Sum}\ \textrm{of}\ \textrm{Sentiment}\ \textrm{Score}\left(\textrm{p},\textrm{c}\right)\ }{\textrm{Number}\ \textrm{of}\ \textrm{studies}\left(\textrm{p},\textrm{c}\right)}\)  

Fig. 12
figure 12

Decision-Making Framework – Cell Score

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Appendix 6: Quality Assessment Checklists

Table 7 presents Dyba et al.’s quality assessment (2007), which was used for non-SLR studies. The scoring values are: “Yes” = 1, “No” = 0 for each screening question. A study that scored “No” on the first screening question or on the second and third screening questions, was excluded as these screening questions represent the minimum quality threshold of the review (Dybå and Dingsøyr 2008).

Table 7 Quality assessment questionnaire used for non-SLR studies (Dyba et al. 2007)
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Nurdiani et al.’s quality assessment checklist (2016), that served to assess the SLR studies presented in Table 8. The scoring values were: “Yes” = 1, “Partially” = 0.5, “No” = 0. The authors conducted the quality assessment separately and independently, and later compared and discussed the results to resolve any disagreement points that came up.

Table 8 Quality assessment questionnaire for SLR studies (Nurdiani et al. 2016)
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Table 9 presents metadata’ format of the extracted items-studies.

Table 9 Metadata’ format of the extracted studies
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Table 10 presents the format used to represent each of the sentiment found. Each study may express a variety of sentiments, each of them is saved in the format defined in the table.

Table 10 The format used to represent each sentiment discovered in the text
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Itzik, D., Roy, G. Does agile methodology fit all characteristics of software projects? Review and analysis. Empir Software Eng 28, 105 (2023). https://doi.org/10.1007/s10664-023-10334-7

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