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

p66Shc Aging Protein in Control of Fibroblasts Cell Fate

Reactive oxygen species (ROS) are wieldy accepted as one of the main factors of the aging process. These highly reactive compounds modify nucleic acids, proteins and lipids and affect the functionality of mitochondria in the first case and ultimately of the cell. Any agent or genetic modification that affects ROS production and detoxification can be expected to influence longevity. On the other hand, genetic manipulations leading to increased longevity can be expected to involve cellular changes that affect ROS metabolism. The 66-kDa isoform of the growth factor adaptor Shc (p66Shc) has been recognized as a relevant factor to the oxygen radical theory of aging. The most recent data indicate that p66Shc protein regulates life span in mammals and its phosphorylation on serine 36 is important for the initiation of cell death upon oxidative stress. Moreover, there is strong evidence that apart from aging, p66Shc may be implicated in many oxidative stress-associated pathologies, such as diabetes, mitochondrial and neurodegenerative disorders and tumorigenesis. This article summarizes recent knowledge about the role of p66Shc in aging and senescence and how this protein can influence ROS production and detoxification, focusing on studies performed on skin and skin fibroblasts

Aberrant Mitochondrial Homeostasis in the Skeletal Muscle of Sedentary Older Adults

The role of mitochondrial dysfunction and oxidative stress has been extensively characterized in the aetiology of sarcopenia (aging-associated loss of muscle mass) and muscle wasting as a result of muscle disuse. What remains less clear is whether the decline in skeletal muscle mitochondrial oxidative capacity is purely a function of the aging process or if the sedentary lifestyle of older adult subjects has confounded previous reports. The objective of the present study was to investigate if a recreationally active lifestyle in older adults can conserve skeletal muscle strength and functionality, chronic systemic inflammation, mitochondrial biogenesis and oxidative capacity, and cellular antioxidant capacity. To that end, muscle biopsies were taken from the vastus lateralis of young and age-matched recreationally active older and sedentary older men and women (N = 10/group; ♀  =  ♂). We show that a physically active lifestyle is associated with the partial compensatory preservation of mitochondrial biogenesis, and cellular oxidative and antioxidant capacity in skeletal muscle of older adults. Conversely a sedentary lifestyle, associated with osteoarthritis-mediated physical inactivity, is associated with reduced mitochondrial function, dysregulation of cellular redox status and chronic systemic inflammation that renders the skeletal muscle intracellular environment prone to reactive oxygen species-mediated toxicity. We propose that an active lifestyle is an important determinant of quality of life and molecular progression of aging in skeletal muscle of the elderly, and is a viable therapy for attenuating and/or reversing skeletal muscle strength declines and mitochondrial abnormalities associated with aging

Manganese Superoxide Dismutase: Guardian of the Powerhouse

The mitochondrion is vital for many metabolic pathways in the cell, contributing all or important constituent enzymes for diverse functions such as β-oxidation of fatty acids, the urea cycle, the citric acid cycle, and ATP synthesis. The mitochondrion is also a major site of reactive oxygen species (ROS) production in the cell. Aberrant production of mitochondrial ROS can have dramatic effects on cellular function, in part, due to oxidative modification of key metabolic proteins localized in the mitochondrion. The cell is equipped with myriad antioxidant enzyme systems to combat deleterious ROS production in mitochondria, with the mitochondrial antioxidant enzyme manganese superoxide dismutase (MnSOD) acting as the chief ROS scavenging enzyme in the cell. Factors that affect the expression and/or the activity of MnSOD, resulting in diminished antioxidant capacity of the cell, can have extraordinary consequences on the overall health of the cell by altering mitochondrial metabolic function, leading to the development and progression of numerous diseases. A better understanding of the mechanisms by which MnSOD protects cells from the harmful effects of overproduction of ROS, in particular, the effects of ROS on mitochondrial metabolic enzymes, may contribute to the development of novel treatments for various diseases in which ROS are an important component