Embedded Software Development Projects Research Articles

Machine Learning Classification to Effort Estimation for Embedded Software Development Projects

This paper discusses the effect of classification in estimating the amount of effort (in man-days) associated with code development. Estimating the effort requirements for new software projects is especially important. As outliers are harmful to the estimation, they are excluded from many estimation models. However, such outliers can be identified in practice once the projects are completed, and so they should not be excluded during the creation of models and when estimating the required effort. This paper presents classifications for embedded software development projects using an artificial neural network (ANN) and a support vector machine. After defining the classifications, effort estimation models are created for each class using linear regression, an ANN, and a form of support vector regression. Evaluation experiments are carried out to compare the estimation accuracy of the model both with and without the classifications using 10-fold cross-validation. In addition, the Games-Howell test with one-way analysis of variance is performed to consider statistically significant evidence.

Estimating Interval of the Number of Errors for Embedded Software Development Projects

Previous investigation focused on the prediction of total and errors for embedded software development projects using an artificial neural network (ANN). However, methods using ANNs have reached their improvement limits, since an appropriate value is estimated using what is known as point estimation in statistics. This paper proposes a method for predicting the number of errors for embedded software development projects using interval estimation provided by a support vector machine and ANN.

Application of Error Estimating Model to Control Quality in Embedded Software Development

In this paper, we propose an errors estimation model for embedded software development projects and implement a model visual tool for processing projects with upper and lower limits from estimated errors. The models are derived by multiple regression analysis, because this way is well known and easily used by developers. In addition, we perform evaluate to conform the models effectiveness.

How to steer an embedded software project: tactics for selecting the software process model

Modern large new product developments (NPD) are typically characterized by many uncertainties and frequent changes. Often the embedded software development projects working on such products face many problems compared to traditional, placid project environments. One of the major project management decisions is then the selection of the project's software process model. An appropriate process model helps coping with the challenges, and prevents many potential project problems. On the other hand, an unsuitable process choice causes additional problems. This paper investigates the software process model selection in the context of large market-driven embedded software product development for new telecommunications equipment. Based on a quasi-formal comparison of publicly known software process models including modern agile methodologies, we propose a process model selection frame, which the project manager can use as a systematic guide for (re)choosing the project's process model. A novel feature of this comparative selection model is that we make the comparison against typical software project problem issues. Some real-life project case examples are examined against this model. The selection matrix expresses how different process models answer to different questions, and indeed there is not a single process model that would answer all the questions. On the contrary, some of the seeds to the project problems are in the process models themselves. However, being conscious of these problems and pitfalls when steering a project enables the project manager to master the situation.