Implementation of Software Process Improvement Through TSPi in Very Small Enterprises

This article shows an experience in a very small enterprise related to improving software quality in terms of test and process productivity. A customized process from the current organizational process based on TSPi was defined and the team was trained on it. The pilot project had schedule and budget constraints. The process began by gathering historical data from previous projects in order to get a measurement repository. Then the project was launched and some metrics were collected. Finally, results were analyzed and the improvements verified

Towards Automated Performance Bug Identification in Python

Context: Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement/marketing domain. Goal: Find a simple and easy to implement solution, predicting performance bugs. Method: We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian Networks, and Logistic Regression. Results: Our empirical results show that a C4.5 model, using lines of code changed, file's age and size as explanatory variables, can be used to predict performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection. Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs

A review of miniaturised Non-Destructive Testing technologies for in-situ inspections

Non-destructive testing (NDT) techniques have become attractive trends of product manufacturing, installation and post-maintenance in the aerospace, automotive and manufacturing industry, because of its benefits such as cost saving, easy to use and high efficiency etc. With the industrial products becoming large-scale, high integration and complication, developing the NDT miniaturisation technique for in-situ inspections is highly demanded and becoming an inevitable trend. However, in-situ inspection using NDT have been limited by a number of factors, such as the heavy weight, large size or complex structure etc. This paper aims to systematically identify and analyse the current state-of-the-art of NDT miniaturisation techniques in research and innovation, and discuss the challenge and prospect of miniaturisation of the commonly used NDT techniques

Software Inspection Team Formation Based on the Learning Style of Individual Inspectors

To improve the software quality, researchers have focused their effort on developing and validating effective methods of finding and fixing defects early in the development process. Software inspections are most widely used defect detection method. Also, researchers showed that the overall effectiveness of an inspection team is affected by the effectiveness of individual inspectors. But researchers have not been able to completely understand the inherent characteristic that makes an individual inspector effective. This paper investigates this problem by analyzing the learning style (LS) preferences of individuals who make up inspection team. Also presents a tool that provides ability to researchers to study the relationship between inspectors? LS and his/her effectiveness in uncovering defects in software requirement document. Cluster and Discriminant analysis techniques were used to sort inspection teams based on their LS preferences. Researchers can use this tool to study further correlations between inspector?s LS and their performance in team