Fig 4 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Content may be subject to copyright.
Source publication
![Fig. 2. Overview of different cooperation types[8]](publication/314301379/figure/fig2/AS:472904233164803@1489760563746/Overview-of-different-cooperation-types8_Q320.jpg)
Global Software Development (GSD) is becoming increasingly prevalent, with software development teams being distributed around the world and working in collaboration with partner companies despite geographic and time differences. The main advantage of GSD which makes it attractive is the greater availability of human resources at lower costs. Howev...
Similar publications
Companies will know the needs, ideals, advantages and lack of self. Especially if this is associated with order fulfillment management in a company, especially in SMB = small medium business.Development for order fulfillment and customer service capabilities, to get measurement right. Measurement provides the road map for helping SMB from as-is cap...
Citations
... Avoiding budget overruns and late software delivery requires accurate cost and effort estimation [4]. Several tools and techniques for cost estimation were developed for the collocated context before the GSD concept, but these tools and techniques lack GSD cost drivers [5]. These cost estimation models are generally classified into algorithmic, non-algorithmic, and hybrid models [6]. ...
... These cost estimation models are generally classified into algorithmic, non-algorithmic, and hybrid models [6]. Estimating effort and cost for GSD projects can be difficult due to a number of issues [5]. Project managers' primary concern is cost estimation. ...
... Furthermore, it was ensured that these databases included all primary research articles on the SLR topic. Wickramaarachchi and Lai [5], Khan et al. [16] have used these databases to find primary research. The next step was searching by consulting references. ...
p>Global software development (GSD) is a well - established discipline of software engineering that focuses on the advantage of a global environment. Effective cost estimation is critical for the success of GSD projects. Cost estimation in a GSD environment is a challenging task. As a re sult, GSD must emphasize cost estimation. Findings show that a number of researchers over the past few decades have emphasized GSD - based cost estimation in GSD; to the best of our knowledge, however, existing cost estimation have not taken into account man y GSD - based cost drivers that must be considered when estimating costs. Motivated by all this, the purpose of this study is to review the existing GSD - based cost estimation models/techniques and cost drivers that influence the accuracy of cost estimation. To identify and compile relevant research papers, a systematic literature review was carried out. From twenty - seven selected studies, initially, 86 GSD - based cost drivers and 12 GSD - based cost estimation models/techniques were extracted. After filtration, 26 cost drivers were identified as significant and to be considered in GSD - based cost estimation. This study significantly identifies GSD - based cost drivers and existing cost estimation techniques.</p
... However, when comprehensive details about the project, including its designs and environment, are accessible, the postarchitecture sub-model will be applied. COCOMO was not among the strategies identified in the research reviewed in a thorough literature analysis on effort estimation in agile software development (Wickramaarachchi and Lai 2017). 5.26 percent of respondents to a similar survey on effort estimation said they utilized COCOMO, but only in conjunction with other methodologies (Britto et al. 2015). ...
Scientific communities are still motivated to create novel approaches and methodologies for early estimation of software project development efforts and testing efforts in soft computing environments due to scheduling and budgetary concerns. Therefore, the software engineering prediction problems (SEPPs) are formulated as machine learning (ML) models with the aim of addressing these issues. In such methodologies that may exhibit significant limitations and drawbacks, efficient metaheuristic approaches are essential to improving prediction performance. Accordingly, this study aims to address software test effort prediction (STP) and software development effort prediction (SEP) with the aim of maximizing prediction accuracy, which in turn minimizes overall project costs and optimizes resource allocation. To achieve this goal, we developed several ML models composed of a backpropagation neural network (BPNN). The proposed models contain the Salp Swarm Algorithm (SSA), which is utilized to replace the traditional network training method and tackle its limitations. The models also contain the great deluge (GD) local search algorithm, which is hybridized with the SSA algorithm to enhance optimization capabilities by finding more balance between exploration and exploitation. During the validation stage of this study, fourteen benchmark datasets were utilized to evaluate the developed models for each of the respective problems. The obtained results were quantified using eight performance metrics and compared across two sections. In the first section, a comparison was made between the results of the hybrid-developed model (HSSA) and those of the standard SSA algorithm and BPNN. In the second comparison, the performance of the HSSA model was compared with several contemporary techniques that are considered state-of-the-art. The evaluation shows that the HSSA performs better than related approaches in most cases for both problems. Finally, additional analysis was performed on the collected results, including examinations of statistical significance, distribution through box plots, and model convergence behavior.
... IoT [50, 60, 71, 131, 170, 38, 47, 180, 191, 237, 298, 22, 26, 29, 55, 74, 73, 120, 157, 238, 250, 278, 4, 16, 19, 30, 77, 92, 142, 147, 153, 159, 162, 168, 183, 201, 217, 251, 261, 264, 283, 289, 295, 5, 48, 87, 95, 106, 109, 112, 114, 126, 135, 138, 149, 143, 145, 150, 155, 160, 175, 176, 184, 198, 223, 230, 234, 239, 249, 263, 275, 276, 282, 284, 288, 294, 303, 305, 306, 3, 13, 10, 24, 25, 28, 43, 45, 83, 84, 89, 107, 163, 195, 197, ?, 210, 214, 235, 245, 247, 265, 266, 274, 279, 281, 293, 300, 308, 76, 94, 103, 116, 158] IoE 42 [144,242] e-commerce [272,246,7,79,204,218,61,88,132,141,82,301,1,70,93,115,133,219,292,33,62,68,97,113,195,212,216,229,235] Supply chain [54,125,36,64,110,174,193,235,296] e-business [302,310,220] e-commerce -electronic services -eservice [271] e-government 43 [17,232,256,50,123,286,8,122,186,189,177,190,257,124,178,269,105,166,203,206,221,254,40,41,81,99,252,267,270] e-logistics 44 [227] Other internet [58,181,192,209 [532,422,534,533,594,648,1102,25,140,1085,764,172,206,1050,342,488,344,415,490,621,1090,21,192,784,947,968,824,1086,92,554,70,71,97,555,746,904,1064,83,338,370,659,747,47,141,195,416,567,568,87] Software project -Software development [532, 534, 533, 578, 579, 707, 1039, 341, 595, 20, 543, 611, 688, 876, 925, 42, 153, [528,838,337,529,579,269,365,837,270,323,537,677,200,252,676,1002,206,234,283,131,628,729,618] Global software engineering [514,244,603,642] Virtual software development 49 [945,1115] 47 The concept does not exists in the CSO. 48 The concept does not exists in the CSO. ...
... The 2017 systematic review of Wickramaarachchi et al. [18] on effort estimation in global software development found that the proposed models for traditional software development were outdated and not applicable to today's emerging technology such as component-based software development, service-oriented architecture, OSS development and enterprise resource planning. The 2015 review of Kaur et al. [19] on software maintenance issues highlighted many ways to reduce maintenance cost and effort such as welldefined and documented software architecture, as well as the use of guidelines and testing quality to create an environment that promotes design consistency. ...
... [22], [23]. Besides, these reviews were conducted for different purposes: the evolution of OSS [14], [5], software effort estimation in general including studies about development and maintenance effort [7], [15], software development or cost estimation not including software maintenance effort [16], [17], [18], software maintenance effort estimation [19], [20], [21] and MEE for OSS [4]. It can be seen that only the SLR [4] published in 2016 addressed our review topic, O-MEE. ...
Background
Software maintenance is known as a laborious activity in the software lifecycle and often considered more expensive than other activities. Open-source software (OSS) has gained considerable acceptance in the industry recently, and the maintenance effort estimation (MEE) of such software has emerged as an important research topic. In this context, researchers have conducted many open-source software maintenance effort estimation (O-MEE) studies based on statistical as well as machine learning techniques for better estimation.
Objective
The objective of this study is to perform a systematic literature review (SLR) to analyze and summarize the empirical evidence of O-MEE ML techniques in current research through a set of five research questions (RQs) related to several criteria (e.g. data pre-processing tasks, data mining tasks, tuning parameter methods, accuracy criteria and statistical tests, as well as ML techniques reported in the literature that outperformed).
Method
We performed a systematic literature review of 36 primary empirical studies published from 2000 to June 2020, selected based on an automated search of six digital databases.
Results
The findings show that bayesian networks, decision tree, support vector machine and instance-based reasoning were the ML techniques most used; few studies opted for ensemble or hybrid techniques. Researchers have paid less attention to O-MEE data pre-processing in terms of feature selection, methods that handle missing values and imbalanced datasets, and tuning parameters of ML techniques. Classification data mining is the task most addressed using different accuracy criteria such as Precision, Recall, and Accuracy, as well as Wilcoxon and Mann-Whitney statistical tests.
Conclusion
This SLR identifies a number of gaps in the current research and suggests areas for further investigation. For instance, since OSS includes different data source formats, researchers should pay more attention to data pre-processing and develop new models using ensemble techniques since they have proved to perform better.
... The existing work lacks in considering the additional cost drivers of GSD. Most of the existing techniques lack quantifying the factors and validation of the generated results [8]. Motivated by this, current research focuses on providing a GSD-specific cost estimation model based on the additional cost drivers of GSD to provide more accurate and realistic estimates. ...
... • Only 50% of the outsourcing in the near future will be successful [8] [11]. • Half of the software companies that shifted their process to GSD failed to generate the expected financial benefits [11]. ...
... • Half of the software companies that shifted their process to GSD failed to generate the expected financial benefits [11]. • 70 percent of the software companies had a significant negative experience with out-sourcing [8]. • In a survey of 50 companies, about 14% of outsourcing operations have deemed a failure [8]. ...
Global Software Development (GSD) projects comprise several critical cost drivers that affect the overall project cost and budget overhead. Thus, there is a need to amplify the existing model in GSD context to reduce the risks associated with cost overhead. Motivated by this, the current work aims at amplifying the existing algorithmic model with GSD cost drivers to get efficient estimates in the context of GSD. To achieve the targeted research objective, current state-of-the-art cost estimation techniques and GSD models are reported. Furthermore, the current study has proposed a conceptual framework to amplify the algorithmic COCOMO-II model in the GSD domain to accommodate additional cost drivers empirically validated by a systematic review and industrial practitioners. The main phases of amplification include identifying cost drivers, categorizing cost drivers, forming metrics, assignment of values, and finally altering the base model equation. Moreover, the proposed conceptual model’s effectiveness is validated through expert judgment, case studies, and Magnitude of Relative Estimates (MRE). The obtained estimates are efficient, quantified, and cover additional GSD aspects than the existing models; hence we could overcome the GSD project’s overall risk by implementing the model. Finally, the results indicate that the model needs further calibration and validation.
... There are many tools and techniques available for the estimation of the cost. Many models are developed before the GSD concept, so these techniques lack the factors and the cost drivers associated with this development [5]. We are uncertain about the applicability of these techniques in GSD. ...
... The estimation techniques are high-level approaches adopted to estimate a project, i.e., automated, semi-automated, model-driven, or regression-based [4]. Simultaneously, the estimation models are more specified, corresponding to the particular mechanism for accurate estimation like COCOMO II based, cobra based, or machine learning-based [5]. Based on selected estimation techniques, the estimation models are created. ...
... Figure 2 represents some statistics regarding the cost of Offshoring that how these additional factors could be misleading for the overall development if not properly analyzed and evaluated. The statistics are extracted from the existing literature [5]. [5] The statistics depicted in Figure 2 provided us motivation to work in this area and to improve the aspects that are lacking to save the cost of Offshoring. ...
Software organization always aims at developing a quality software product using the estimated development resources, effort, and time. Global Software Development (GSD) has emerged as an essential tool to ensure optimal utilization of resources, which is performed in globally distributed settings in various geographical locations. Global software engineering focuses on reducing the cost, increasing the development speed, and accessing skilled developers worldwide. Estimating the required amount of resources and effort in the distributed development environment remains a challenging task. Thus, there is a need to focus on cost estimation models in the GSD context. We nevertheless acknowledge that several cost estimation techniques have been reported. However, to the best of our knowledge, the existing cost estimation techniques/models lack considering the additional cost drivers required to compute the accurate cost estimation in the GSD context. Motivated by this, the current work aims at identifying the other cost drivers that affect the cost estimation in the context of GSD. To achieve the targeted objectives, current state-of-the-art related to existing cost estimation techniques of GSD is reported. We adopted SLR and Empirical approach to address the formulated research questions. The current study also identifies the missing factors that would help the practitioners improve the cost estimation models. The results indicate that previously conducted work ignores the additional elements necessary for the cost estimation in the GSD context. Moreover, the current work proposes a conceptual cost estimation model tailored to fit the GSD context.
... Idri et al. [42] performed a systematic review on ensemble effort estimation (EEE) technique, a combination of the existing models of software development effort estimation, which covered papers from 2000 to 2016. Wickramaarachchi and Lai [77] searched for literature related to effort estimation on Global Software Development. As these studies focused their attention on general aspects of cost/effort models, they did not present information on software security aspects. ...
Building more secure software is a recent concern for software engineers due to increasing incidences of data breaches and other types of cyber attacks. However, software security, through the introduction of specialized practices in the software development life cycle, leads to an increase in the development cost. Although there are many studies on software cost models, few address the additional costs required to build secure software. We conducted a systematic review in the form of a mapping study to classify and analyze the literature related to the impact of security in software development costs. Our search strategy strove to achieve high completeness by the identification of a quasi-gold-standard set of papers, which we then used to establish a search string and retrieve papers from research databases automatically. The application of inclusion/exclusion criteria resulted in a final set of 54 papers, which were categorized according to the approach to software security cost analysis. Perform Security Review, Apply Threat Modeling, and Perform Security Testing were the three most frequent activities related to cost, and Common Criteria was the most applied standard. We also identified ten approaches to estimating software security costs for development projects; however, their validation remains a challenge, which could be addressed in future studies.
... Besides that, some costs may need to be invested for project management tools in order to manage the project in distributed settings [6], [34] and test tools to manage the testing activities in particular [7], [35]. The project may also need to consider the costs for other facilities such as the storage room and office space which are commonly required to store physical materials generated throughout the project [19], [36]. ...
... Table 1 summarizes the categories and its reference's sources. Fig. 1 presents the findings in a diagram form and the results of data analysis are presented in the following paragraphs Test case complexity [15], [17] Test type [17] Test change frequency [7], [12] Infrastructure Communication tools [3], [7], [26], [33] Repository tools Management tools [6], [34] Test tools [7], [35] Facilities [19], [36] Travelling Transportation [19] Accommodation [21] Immigration [19] Human Resource ...
Software testing outsourcing appears to be the best alternative to acquire better software quality with competent ratification by extrinsic parties who have the capability to do it. Through the effort, organizations are peeking to promising benefits constitute in it such as current testing technology, experts, an abridgment of the project's duration and more concentration on the main organisation's activity. Along with these benefits, one important reason that encourages the decision is optimization of cost expenditure, which the strategy is perceived as a good move for a competitive organization. However, implementing such preference eventually results in a different outcome. Organizations have to bear the higher cost and incur losses of cost deviation from the expected estimation. The conflicting between cost and benefits raises an important concern of striving better cost estimation for such projects. This paper aims to address this interest by analyzing the existing literature in order to identify the contributing factors towards better cost estimation for software testing outsourcing project-context. The analysis is done using the content analysis method. The results could be divided into two categories; which are the cost items and contributing factors. Cost items consist of direct cost and indirect cost, which refers to the expenses for the project. While the contributing factors consist of people and environment, which are needed to produce accurate cost estimation. The findings provide an insight to excogitate attentively the essentials in the endeavor of improving the exactitude of cost estimation for software testing outsourcing project.
The construction phase effort prediction is needed for assigning resources to teams of practitioners destined specifically to this phase of the software development life cycle (SDLC). Construction effort (CE) has been reported between 27.5% and 58% of the total SDLC effort causing the uncertainty of taking these percentages as reference. A support vector regression (SVR) training involves quadratic programming problems that can analytically be solved using a sequential minimal optimization (SMO) algorithm. Moreover, a Pearson VII (PUK) kernel is useful to replace a set of kernel functions commonly used by a SVR. The objective of this study is to apply the SMO with the PUK to train SVR for predicting CE. The SVR model trained with the SMO algorithm having as kernel to the PUK (SVR‐SMO‐PUK) prediction accuracy was statistically compared to those accuracies obtained from statistical regression (SR), neural network (NN), and two types of SVR. Seven international public data sets of software projects were used. Results showed that the SVR‐SMO‐PUK was better than the SR in five data sets and better than NN in two of these five data sets. It was equal than SR and NN in the remaining two data sets. It was equal than ε‐SVR and ʋ‐SVR in the seven data sets. Thus, the SVR‐SMO‐PUK is useful to software managers to predict CE.
