Predicting Software Defects Based on Cognitive Error Theories

As the primary cause of software defects, human error is the key to understanding and perhaps to predicting and preventing software defects. However, little research has been done to forecast software defects based on defects' cognitive nature. This paper proposes an idea for predicting software defects by applying the current scientific understanding of human error mechanisms. This new prediction method is based on the main causal mechanism underlying software defects: an error-prone scenario triggers a sequence of human error modes. Preliminary evidence for supporting this idea is presented

Punctuated Equilibrium in Software Evolution

The approach based on paradigm of self-organized criticality proposed for experimental investigation and theoretical modelling of software evolution. The dynamics of modifications studied for three free, open source programs Mozilla, Free-BSD and Emacs using the data from version control systems. Scaling laws typical for the self-organization criticality found. The model of software evolution presenting the natural selection principle is proposed. The results of numerical and analytical investigation of the model are presented. They are in a good agreement with the data collected for the real-world software.Comment: 4 pages, LaTeX, 2 Postscript figure

A research review of quality assessment for software

Measures were recommended to assess the quality of software submitted to the AdaNet program. The quality factors that are important to software reuse are explored and methods of evaluating those factors are discussed. Quality factors important to software reuse are: correctness, reliability, verifiability, understandability, modifiability, and certifiability. Certifiability is included because the documentation of many factors about a software component such as its efficiency, portability, and development history, constitute a class for factors important to some users, not important at all to other, and impossible for AdaNet to distinguish between a priori. The quality factors may be assessed in different ways. There are a few quantitative measures which have been shown to indicate software quality. However, it is believed that there exists many factors that indicate quality and have not been empirically validated due to their subjective nature. These subjective factors are characterized by the way in which they support the software engineering principles of abstraction, information hiding, modularity, localization, confirmability, uniformity, and completeness

An Innovative Approach for Predicting Software Defects by Handling Class Imbalance Problem

From last decade unbalanced data has gained attention as a major challenge for enhancing software quality and reliability. Due to evolution in advanced software development tools and processes, today’s developed software product is much larger and complicated in nature. The software business faces a major issue in maintaining software performance and efficiency as well as cost of handling software issues after deployment of software product. The effectiveness of defect prediction model has been hampered by unbalanced data in terms of data analysis, biased result, model accuracy and decision making. Predicting defects before they affect your software product is one way to cut costs required to maintain software quality. In this study we are proposing model using two level approach for class imbalance problem which will enhance accuracy of prediction model. In the first level, model will balance predictive class at data level by applying sampling method. Second level we will use Random Forest machine learning approach which will create strong classifier for software defect. Hence, we can enhance software defect prediction model accuracy by handling class imbalance issue at data and algorithm level

The impact of developer team sizes on the structural attributes of software

It is established that the internal quality of software is a key determinant of the total cost of ownership of that software. The objective of this research is to determine the impact that the development team’s size has on the internal structural attributes of a codebase and, in doing so, we consider the impact that the team’s size may have on the internal quality of the software that they produce. In this paper we leverage the wealth of data available in the open-source domain by mining detailed data from 1000 projects in Google Code and, coupled with one of the most established of object-oriented metric suites, we isolate and identify the effect that the development team size has on internal structural attributes of the software produced. We will find that some measures of functional decomposition are enhanced when we compare projects authored by fewer developers against those authored by a larger number of developers while measures of cohesion and complexity are degraded

BMR: Benchmarking Metrics Recommender for personnel issues in software development proyects

This paper presents an architecture which applies document similarity measures to the documentation produced during the phases of software development in order to generate recommendations of process and people metrics for similar projects. The application makes a judgment of similarity of the Service Provision Offer (SPO) document of a new proposed project to a collection of Project History Documents (PHD), stored in a repository of unstructured texts. The process is carried out in three stages: firstly, clustering of the Offer document with the set of PHDs which are most similar to it; this provides the initial indication of whether similar previous projects exist, and signifies similarity. Secondly, determination of which PHD in the set is most comparable with the Offer document, based on various parameters: project effort, project duration (time), project resources (members/size of team), costs, and sector(s) involved, indicating comparability of projects. The comparable parameters are extracted using the GATE Natural Language Processing architecture. Lastly, a recommendation of metrics for the new project is made, which is based on the transferability of the metrics of the most similar and comparable PHD extracted, here referred to as recommendation.This work is supported by the Spanish Ministry of Industry, Tourism, and Commerce under the project SONAR (TSI-340000-2007-212), GODO2 (TSI- 020100-2008-564) and SONAR2 (TSI-020100-2008- 665) and the MID-CBR project of the Spanish Committee of Education & Science (TIN2006-15140- C03-02).Publicad