Statistical Process Control for Software: Fill the Gap

The characteristic of software processes, unlike manufacturing ones, is that they have a very high human-centered component and are primarily based on cognitive activities. As so, each time a software process is executed, inputs and outputs may vary, as well as the process performances. This phenomena is better identified in literature with the terminology of “Process Diversity” (IEEE, 2000). Given the characteristics of a software process, its intrinsic diversity implies the difficulty to predict, monitor and improve it, unlike what happens in other contexts. In spite of the previous observations, Software Process Improvement (SPI) is a very important activity that cannot be neglected. To face these problems, the software engineering community stresses the use of measurement based approaches such as QIP/GQM (Basili et al., 1994) and time series analysis: the first approach is usually used to determine what improvement is needed; the time series analysis is adopted to monitor process performances. As so, it supports decision making in terms of when the process should be improved, and provides a manner to verify the effectiveness of the improvement itself. A technique for time series analysis, well-established in literature, which has given insightful results in the manufacturing contexts, although not yet in software process ones is known as Statistical Process Control (SPC) (Shewhart, 1980; Shewhart, 1986). The technique was originally developed by Shewhart in the 1920s and then used in many other contexts. The basic idea it relies on consists in the use of so called “control charts” together with their indicators, called run tests, to: establish operational limits for acceptable process variation; monitor and evaluate process performances evolution in time. In general, process performance variations are mainly due to two types of causes classified as follows:  Common cause variations: the result of normal interactions of people, machines, environment, techniques used and so on.  Assignable cause variations: arise from events that are not part of the process and make it unstable. In this sense, the statistically based approach, SPC, helps determine if a process is stable or not by discriminating between common cause variation and assignable cause variation. We can classify a process as “stable” or “under control” if only common causes occur. More precisely, in SPC data points representing measures of process performances are collected. These values are then compared to the values of central tendency, upper and lower limit of admissible performance variations. While SPC is a well established technique in manufacturing contexts, there are only few works in literature (Card, 1994; Florac et al., 2000; Weller, 2000(a); Weller, 2000(b); Florence, 2001; Sargut & Demirors, 2006; Weller, & Card. 2008; Raczynski & Curtis, 2008) that present successful outcomes of SPC adoption to software. In each case, not only are there few cases of successful applications but they don’t clearly illustrate the meaning of control charts and related indicators in the context of software process application. Given the above considerations, the aim of this work is to generalize and put together the experiences collected by the authors in previous studies on the use of Statistical Process Control in the software context (Baldassarre et al, 2004; Baldassarre et al, 2005; Caivano 2005; Boffoli, 2006; Baldassarre et al, 2008; Baldassarre et al, 2009) and present the resulting stepwise approach that: starting from stability tests, known in literature, selects the most suitable ones for software processes (tests set), reinterprets them from a software process perspective (tests interpretation) and suggest a recalculation strategy for tuning the SPC control limits. The paper is organized as follows: section 2 briefly presents SPC concepts and its peculiarities; section 3 discusses the main differences and lacks of SPC for software and presents the approach proposed by the authors; finally, in section 4 conclusions are drawn

Towards Knowledge Based Risk Management Approach in Software Projects

All projects involve risk; a zero risk project is not worth pursuing. Furthermore, due to software project uniqueness, uncertainty about final results will always accompany software development. While risks cannot be removed from software development, software engineers instead, should learn to manage them better (Arshad et al., 2009; Batista Webster et al., 2005; Gilliam, 2004). Risk Management and Planning requires organization experience, as it is strongly centred in both experience and knowledge acquired in former projects. The larger experience of the project manager improves his ability in identifying risks, estimating their occurrence likelihood and impact, and defining appropriate risk response plan. Thus risk knowledge cannot remain in an individual dimension, rather it must be made available for the organization that needs it to learn and enhance its performances in facing risks. If this does not occur, project managers can inadvertently repeat past mistakes simply because they do not know or do not remember the mitigation actions successfully applied in the past or they are unable to foresee the risks caused by certain project restrictions and characteristics. Risk knowledge has to be packaged and stored over time throughout project execution for future reuse. Risk management methodologies are usually based on the use of questionnaires for risk identification and templates for investigating critical issues. Such artefacts are not often related each other and thus usually there is no documented cause-effect relation between issues, risks and mitigation actions. Furthermore today methodologies do not explicitly take in to account the need to collect experience systematically in order to reuse it in future projects. To convey these problems, this work proposes a framework based on the Experience Factory Organization (EFO) model (Basili et al., 1994; Basili et al., 2007; Schneider & Hunnius, 2003) and then use of Quality Improvement Paradigm (QIP) (Basili, 1989). The framework is also specialized within one of the largest firms of current Italian Software Market. For privacy reasons, and from here on, we will refer to it as “FIRM”. Finally in order to quantitatively evaluate the proposal, two empirical investigations were carried out: a post-mortem analysis and a case study. Both empirical investigations were carried out in the FIRM context and involve legacy systems transformation projects. The first empirical investigation involved 7 already executed projects while the second one 5 in itinere projects. The research questions we ask are: Does the proposed knowledge based framework lead to a more effective risk management than the one obtained without using it? Does the proposed knowledge based framework lead to a more precise risk management than the one obtained without using it? The rest of the paper is organized as follows: section 2 provides a brief overview of the main research activities presented in literature dealing with the same topics; section 3 presents the proposed framework, while section 4 its specialization in the FIRM context; section 5 describes empirical studies we executed, results and discussions are presented in section 6. Finally, conclusions are drawn in section 7

Integrating Fitt’s law in heuristical evaluation checklist

The Learner Centered Design (LCD) guarantees efficiency, effectiveness and customer satisfaction avoiding that the learning environment becomes frustrating and offering quality to the IT support together with low cost evaluation parameters. In the academic and industrial environments the heuristic analysis based upon checklist is a diffused method to analyze the LCD adequacy to the quality criteria. The usability of the interfaces has a relevant role. Fitts’ law is a model of the human behavior of the prediction of the times for the pointing of a target. It can be used for the design of usable interfaces, as well. In literature, the empirical evidence shows the effectiveness of Fitts’ law in the interactions between humans and control equipment for the military and aeronautics field. Nevertheless there are still few experiments in web usability. In this paper we present an experiment which shows a significant improvement of the indices used to measure usability. The advantages of the integration of Fitts’ law within the heuristic based upon checklist will be analyzed after an adequate presentation of the literature. A usability test on two samples of users of the Jonian Department at the University of Bari will be adopted

PROMETHEUS: A Web Platform To Support Open Innovation

The competitive pressure for organizations and countries has moved the focus of economy from material to immaterial assets. The recognition that knowledge is the fundamental driver of sustainable competitive and collaborative advantage has been a major breakthrough in management thinking. The movement from internal R&D to external connect and develop opens the door for large and small companies to reach beyond their core competencies to remain competitive in an increasingly complex, uncertain and changing environment. This phenomenon, called Open Innovation, alone has opened completely new perspectives about how to manage human, physical and financial resources. It has also influenced organisations to start recognising that to thrive they need to find new ways of accessing the knowledge they need exactly when they need it, in order to adapt to an ever-changing and increasingly complex and uncertain environment. To this end we propose a platform that is the Knowledge Base Experience (KEB), which collects Knowledge Experience Packages (KEP). This framework is able to support a cooperative “innovation chain” from an Open Innovation (OI) perspective because it contains the tools needed to facilitate the acquisition of knowledge that support the innovation to transfer with particular emphasis on supporting, in particular, Small and Medium companies to survive the current turbulence of the market