Alberto Sillitti

Project management in plan-based and agile companies

Agile methods are a recent set of development techniques that apply a human-centered approach to software production. The agile approach aims to deliver high-quality products faster, producing satisfied customers. We conducted an empirical study to investigate whether agile methods change and improve project management practices in software companies. Survey results show that adopting agile methods appears to improve management of the development process and customer relationships. This article has given a first analysis of the advantages and disadvantages of adopting agile methods from a project management perspective.

Does refactoring improve reusability

The improvement of the software development process through the development and utilization of high quality and reusable software components has been advocated for a long time. Agile Methods promote some interesting practices, in particular the practice of refactoring, which are supposed to improve understandability and maintainability of source code. In this research we analyze if refactoring promotes ad-hoc reuse of object-oriented classes by improving internal quality metrics. We conduct a case study in a close-to industrial, agile environment in order to analyze the impact of refactoring on internal quality metrics of source code. Our findings sustain the hypothesis that refactoring enhances quality and reusability of – otherwise hard to reuse – classes in an agile development environment. Given such promising results, additional experimentation is required to validate and generalize the results of this work.

Requirements Engineering for Agile Methods

Collecting, understanding, and managing requirements is a critical aspect in all development methods. This is true for Agile Methods as well. In particular, several agile practices deal with requirements in order to implement them correctly and satisfy the needs of the customer. These practices focus on a continuous interaction with the customer to address the requirements evolution over time, prioritize them, and deliver the most valuable functionalities first. This chapter introduces Agile Methods as the implementation of the principles of the lean production in software development. Therefore, Agile Methods focus on continuous process improvement through the identification and the removal of waste, whatever does not add value for the customer.

Managing uncertainty in requirements: a survey in documentation-driven and agile companies

This paper investigates commonalities and differences between agile and documentation-driven approaches in managing uncertainty in requirement gathering. The research method is a survey collected interviewing sixteen project managers of Italian software companies, 8 using agile methods, and 8 using documentation-driven methods. The results show that agile and document-driven companies consider in a different way the problem of changing requirements and the related uncertainty; thus, they manage differently requirements gathering and the relationship with the customer

Effort Prediction in Iterative Software Development Processes -- Incremental Versus Global Prediction Models

Estimation of development effort without imposing overhead on the project and the development team is of paramount importance for any software company. This study proposes a new effort estimation methodology aimed at agile and iterative development environments not suitable for description by traditional prediction methods. We propose a detailed development methodology, discuss a number of architectures of such models (including a wealth of augmented regression models and neural networks) and include a thorough case study of Extreme Programming (XP) in two semi-industrial projects. The results of this research evidence that in the XP environment under study the proposed incremental model outperforms traditional estimation techniques most notably in early phases of development. Moreover, when dealing with new projects, the incremental model can be developed from scratch without resorting itself to historic data.

Data description

The study is concerned with a granular data description in which we propose a characterization of numeric data by a collection of information granules so that the key structure of the data, their topology and essential relationships are described in the form of a family of fuzzy sets - information granules. A comprehensive design process is introduced in which we show a two-phase development strategy: first, numeric prototypes are built with the use of Fuzzy C-Means (FCM) that is followed by their augmentation resulting in a collection of information granules. In the design of information granules we engage the fundamental ideas of Granular Computing, especially the principle of justifiable granularity. A series of experiments is presented to visualize the key steps of the construction of information granules.

Failure prediction based on log files using Random Indexing and Support Vector Machines

Research problem: The impact of failures on software systems can be substantial since the recovery process can require unexpected amounts of time and resources. Accurate failure predictions can help in mitigating the impact of failures. Resources, applications, and services can be scheduled to limit the impact of failures. However, providing accurate predictions sufficiently ahead is challenging. Log files contain messages that represent a change of system state. A sequence or a pattern of messages may be used to predict failures. Contribution: We describe an approach to predict failures based on log files using Random Indexing (RI) and Support Vector Machines (SVMs). Method: RI is applied to represent sequences: each operation is characterized in terms of its context. SVMs associate sequences to a class of failures or non-failures. Weighted SVMs are applied to deal with imbalanced datasets and to improve the true positive rate. We apply our approach to log files collected during approximately three months of work in a large European manufacturing company. Results: According to our results, weighted SVMs sacrifice some specificity to improve sensitivity. Specificity remains higher than 0.80 in four out of six analyzed applications. Conclusions: Overall, our approach is very reliable in predicting both failures and non-failures.

Pair Programming and Software Defects--A Large, Industrial Case Study

In the last decade, there has been increasing interest in pair programming (PP). However, despite the existing work, there is still a lack of substantial evidence of the effects of PP in industrial environments. To address this issue, we have analyzed the work of a team of 17 industrial developers for 14 months. The team is part of the IT department of a large Italian manufacturing company; it adopts a customized version of extreme programming (XP). We have investigated the effects of PP on software quality in five different scenarios. The results show that PP appears to provide a perceivable but small effect on the reduction of defects in these settings.

A Case Study on the Impact of Refactoring on Quality and Productivity in an Agile Team

Refactoring is a hot and controversial issue. Supporters claim that it helps increasing the quality of the code, making it easier to understand, modify and maintain. Moreover, there are also claims that refactoring yields higher development productivity --- however, there is only limited empirical evidence of such assumption. A case study has been conducted to assess the impact of refactoring in a close-to industrial environment. Results indicate that refactoring not only increases aspects of software quality, but also improves productivity. Our findings are applicable to small teams working in similar, highly volatile domains (ours is application development for mobile devices). However, additional research is needed to ensure that this is indeed true and to generalize it to other contexts.

A case-study on using an Automated In-process Software Engineering Measurement and Analysis system in an industrial environment

Automated systems for measurement and analysis are not adopted on a large scale in companies, despite the opportunities they offer. The fear of the “Big Brother” and the lack of reports giving insights into the real adoption process and concrete usages in industry are barriers to this adoption. We report on a case-study on the adoption and long-term usage (2 years of running system) of such a system in a company focusing on the adoption process and the related challenges we encountered.