相关论文: Is it Possible to Disregard Obsolete Requirements?…
Effort estimation is a complex area in decision-making, and is influenced by a diversity of factors that could increase the estimation error. The effects on effort estimation accuracy of having obsolete requirements in specifications have…
In this paper, we propose improvements in how estimation bias, e.g., the tendency towards under-estimating the effort, is measured. The proposed approach emphasizes the need to know what the estimates are meant to represent, i.e., the type…
Effort estimation is a key factor for software project success, defined as delivering software of agreed quality and functionality within schedule and budget. Traditionally, effort estimation has been used for planning and tracking project…
Reliable effort estimation remains an ongoing challenge to software engineers. Accurate effort estimation is the state of art of software engineering, effort estimation of software is the preliminary phase between the client and the…
Expert judgment for software effort estimation is oriented toward direct evidences that refer to actual effort of similar projects or activities through experts' experiences. However, the availability of direct evidences implies the…
It is commonly accepted that the quality of requirements specifications impacts subsequent software engineering activities. However, we still lack empirical evidence to support organizations in deciding whether their requirements are good…
According to a study of The Standish Group International, 44% of software projects cost more and last longer than expected. More accurate the effort estimation is; the better the enterprise gets organized and the more the software project…
Offline evaluations of recommender systems attempt to estimate users' satisfaction with recommendations using static data from prior user interactions. These evaluations provide researchers and developers with first approximations of the…
Although software managers are generally good at new project estimation, their experience of scheduling rework tends to be poor. Inconsistent or incorrect effort estimation can increase the risk that the completion time for a project will…
Context:More than half the literature on software effort estimation (SEE) focuses on comparisons of new estimation methods. Surprisingly, there are no studies comparing state of the art latest methods with decades-old approaches.…
An increasing number of software companies have already realized the importance of storing project-related data as valuable sources of information for training prediction models. Such kind of modeling opens the door for the implementation…
Data is a cornerstone of empirical software engineering (ESE) research and practice. Data underpin numerous process and project management activities, including the estimation of development effort and the prediction of the likely location…
While observational data are routinely used to estimate causal effects of biomedical treatments, doing so requires special methods to adjust for observed confounding. These methods invariably rely on untestable statistical and causal…
Context: Early size predictions (ESP) can lead to errors in effort predictions for software projects. This problem is particular acute in parametric effort models that give extra weight to size factors (for example, the COCOMO model assumes…
A key goal of empirical research in software engineering is to assess practical significance, which answers whether the observed effects of some compared treatments show a relevant difference in practice in realistic scenarios. Even though…
As demand for computer software continually increases, software scope and complexity become higher than ever. The software industry is in real need of accurate estimates of the project under development. Software development effort…
Software effort estimation models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software…
Omitted variable bias occurs when a statistical model leaves out variables that are relevant determinants of the effects under study. This results in the model attributing the missing variables' effect to some of the included variables --…
Observational data is often readily available in large quantities, but can lead to biased causal effect estimates due to the presence of unobserved confounding. Recent works attempt to remove this bias by supplementing observational data…
Model selection aims to identify a sufficiently well performing model that is possibly simpler than the most complex model among a pool of candidates. However, the decision-making process itself can inadvertently introduce non-negligible…