Linear Programming as a Baseline for Software Effort Estimation

Software effort estimation studies still suffer from discordant empirical results (i.e., conclusion instability) mainly due to the lack of rigorous benchmarking methods. So far only one baseline model, namely, Automatically Transformed Linear Model (ATLM), has been proposed yet it has not been extensively assessed. In this article, we propose a novel method based on Linear Programming (dubbed as Linear Programming for Effort Estimation, LP4EE) and carry out a thorough empirical study to evaluate the effectiveness of both LP4EE and ATLM for benchmarking widely used effort estimation techniques. The results of our study confirm the need to benchmark every other proposal against accurate and robust baselines. They also reveal that LP4EE is more accurate than ATLM for 17% of the experiments and more robust than ATLM against different data splits and cross-validation methods for 44% of the cases. These results suggest that using LP4EE as a baseline can help reduce conclusion instability. We make publicly available an open-source implementation of LP4EE in order to facilitate its adoption in future studies

Linear programming as a baseline for software effort estimation

Software effort estimation studies still suffer from discordant empirical results (i.e., conclusion instability) mainly due to the lack of rigorous benchmarking methods. So far only one baseline model, namely, Automatically Transformed Linear Model (ATLM), has been proposed yet it has not been extensively assessed. In this article, we propose a novel method based on Linear Programming (dubbed as Linear Programming for Effort Estimation, LP4EE) and carry out a thorough empirical study to evaluate the effectiveness of both LP4EE and ATLM for benchmarking widely used effort estimation techniques. The results of our study confirm the need to benchmark every other proposal against accurate and robust baselines. They also reveal that LP4EE is more accurate than ATLM for 17% of the experiments and more robust than ATLM against different data splits and cross-validation methods for 44% of the cases. These results suggest that using LP4EE as a baseline can help reduce conclusion instability. We make publicly available an open-source implementation of LP4EE in order to facilitate its adoption in future studies

Models and metrics for software management and engineering

This paper attempts to characterize and present a state of the art view of several quantitative models and metrics of the software life cycle. These models and metrics can be used to aid in managing and engineering software projects. They deal with various aspects of the software process and product, including resources allocation and estimation, changes and errors, size, complexity and reliability. Some indication is given of the extent to which the various models have been used and the success they have achieved

Relationship between size, effort, duration and number of contributors in large FLOSS projects

This contribution presents initial results in the study of the relationship between size, effort, duration and number of contributors in eleven evolving Free/Libre Open Source Software (FLOSS) projects, in the range from approx. 650,000 to 5,300,000 lines of code. Our initial motivation was to estimate how much effort is involved in achieving a large FLOSS system. Software cost estimation for proprietary projects has been an active area of study for many years. However, to our knowledge, no previous similar research has been conducted in FLOSS effort estimation. This research can help planning the evolution of future FLOSS projects and in comparing them with proprietary systems. Companies that are actively developing FLOSS may benefit from such estimates. Such estimates may also help to identify the productivity ’baseline’ for evaluating improvements in process, methods and tools for FLOSS evolution

Deep space network software cost estimation model

A parametric software cost estimation model prepared for Jet PRopulsion Laboratory (JPL) Deep Space Network (DSN) Data System implementation tasks is described. The resource estimation mdel modifies and combines a number of existing models. The model calibrates the task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software life-cycle statistics

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

Finding relationships between effort and other variables in the SEL

Estimating the amount of effort required for a software development project is one of the major aspects of resource estimation for that project. In this study, the relationship between effort and other variables for 23 Software Engineering Laboratory (SEL) projects that were developed for NASA/Goddard Space Flight Center was examined. These variables fell into two categories: those which can be determined in the early stages of project development and may therefore be useful in a baseline equation for predicting effort in future projects, and those which can be used mainly to characterize or evaluate effort requirements and thus enhance the understanding of the software development process in this environment. Some results of the analyses are presented