Abstract
The role of decision trees in software development effort estimation (SDEE) has received increased attention across several disciplines in recent years thanks to their power of predicting, their ease of use, and understanding. Furthermore, there are a large number of published studies that investigated the use of a decision tree (DT) techniques in SDEE. Nevertheless, in reviewing the literature, a systematic literature review (SLR) that assesses the evidence stated on DT techniques is still lacking. The main issues addressed in this paper have been divided into five parts: prediction accuracy, performance comparison, suitable conditions of prediction, the effect of the methods employed in association with DT techniques, and DT tools. To carry out this SLR, we performed an automatic search over five digital libraries for studies published between 1985 and 2019. In general, the results of this SLR revealed that most DT methods outperform many techniques and show an improvement in accuracy when combined with association rules (AR), fuzzy logic (FL), and bagging. Additionally, it has been observed a limited use of DT tools: it is therefore suggested for researchers to develop more DT tools to promote the industrial utilization of DT amongst professionals.
Highlights
Much of the greater part of the literature on software project management pays particular attention to software development effort estimation (SDEE)
The majority of studies are based on a history-based type, which means that the evaluation of decision tree (DT) techniques is based on historical software project datasets
The accuracy of these DT estimation techniques may depend on certain categories of parameters which are organized into three different groups: the first concerns the dataset’s characteristics like; the second is about the DT’s structure (Split rule, number of cases per node, depth of the tree, stopping criteria, effort calculation method, etc); the third concerns the employed techniques of evaluation and validation such as
Summary
Much of the greater part of the literature on software project management pays particular attention to SDEE. According to [1] SDEE refers to the process of estimating the necessary effort needed for developing any software with regards to money, timeline, and staffing. The effort's unit is generally expressed in man-day/month/hour [2]. Precise and accurate software cost prediction can result in successful control of the budget, time, and appropriate resource allocation. Overestimating is almost as strong a risk factor for software project failure as underestimating. [3] found that inaccurate estimates of required resources are one of the most common reasons why software projects fail. Helps in analyzing the practicability of any project regarding its cost-effectiveness [4] which ensures its success
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