A method for modeling and evaluating software maintenance process performances

Regression testing is very important process in software maintenance. Unfortunately, it is costly and time consuming to allow for the re-execution of all test cases during regression testing. The challenge in regression testing is the selection of best test cases from the existing test suite.This paper presents an evolutionary regression test case prioritization for object-oriented software based on extended system dependence graph model of the affected program using genetic algorithm. The approach is based on optimization of selected test case from dependency analysis of the source codes. The goal is to identify changes in a method's body due to data dependence, control dependence and dependent due to object relation such as inheritance and polymorphism, select the test cases based on affected statements and ordered them based on their fitness by using GA.The number of affected statements determined how fit a test case is good for regression testing. A case study is reported to provide evidence of the feasibility of the approach and its benefits in increasing the rate of fault detection and reduction in regression testing effort compared with retest-all. It was shown that our approach needs 30% of the test cases to cover all the faults, while 80% is needed to cover all the faults using retest-all, which is time consuming and costly.

A process capability based assessment model for software workforce in emergent software organizations

Modification analysis is an essential phase of most software maintenance processes, requiring decision makers to perform and predict potential change impacts, feasibility and costs associated with a potential modification request. The majority of existing techniques and tools supporting modification analysis focusing on source code level analysis and require an understanding of the system and its implementation. In this research, we present a novel approach to support the identification of potential modification and re-testing efforts associated with a modification request, without the need for analyzing or understanding the system source code. We combine Use Case Maps with Formal Concept Analysis to provide a unique modification analysis framework that can assist decision makers during modification analysis at the requirements level. We demonstrate the applicability of our approach on a telephony system case study.

SUMMARYSoftware process engineering is a discipline, which aims to study and improve software development and maintenance processes. The explicit definition of software processes is essential. To this end, the Object Management Group consortium proposed the Software & Systems Process Engineering Meta‐Model (SPEM) that exploits the benefits of the Model Driven Architecture paradigm applied to software process models, instead of software specification models. The aim of this study is to discover evidence clusters and evidence deserts in the use and application of SPEM from a business process management point of view. To reach the proposed objective, we have undertaken a systematic mapping study of the existing scientific literature.The reviewed literature deals mainly with process modeling and, to a lesser extent, with process adaptability, verification, and validation, enactment and evaluation. Wide agreement exists in using the SPEM meta‐model to develop different types of methods and processes. Further research efforts are needed in areas related to enactment and evaluation of software processes. There is a need to evolve to a new version of the meta‐model that incorporates the improvements proposed by different authors. Copyright © 2013 John Wiley & Sons, Ltd.

Software process is the most important part of software project management, and software process models are important means of implementing the software process in any software organization. When there is need to improve the software process, then the need to improve the work-task part (software process models) of the software process must arise. As, there is need of increasing the commitment of software project managers through out the software development life cycle (SDLC), so we need to give them the list of activities in Waterfall Model, which should be made efficient by the software development team, to get the 80% productivity (output) easily, by reducing the effort of software development and ultimately increasing the performance of the software process. So, the idea of 80/20 rule (Pareto Principle, 1935) can be applied in the software engineering process model, in this regard. The Pareto Principle (1935) directed us to focus on what is really important in any field and field's work. Software process is most important part of software project management (SPM). This is also then ultimately needed for software project management (SPM) improvement. Our main research work is to improve the work-task part of the software process (i.e. software process models by applying the Pareto Principle - 80/20 Rule (1935) (or law of vital few) (1935), in the software engineering process models (or work-task part of the software process). This will be to facilitate the software project manager, using the software process models practically more efficient than without application of 80/20 Rule in software engineering Waterfall model. This has resulted in the reduction of the software development effort and increase in the performance of the software process. The results (efficient and improved software process models) will be implemented in the software industry, by software project managers, by focusing on the list of activities giving 80 percent of the overall productivity of the software process. The results will be the list of activities having the capability to give 20 percent of the overall productivity of the software Waterfall process, to be ignored, eliminated or designated to reduce the effort.

Programmable logic controllers (PLC) have been one of the most widely used controllers in industry. They have a graphical programming language interface resambling the relay panels which they are intended to replace so that the transition from relay panels might be swift and smooth. Although learning this programming language is easy, real life experiments are essential for students to gain greater understanding of the logic behind the language. However, it is generally too expensive to provide an experimental set-up for students to test their theoretical knowledge. For this reason, a conveyor and feeder system was developed as a low cost 3D printable design using a CAD program and implemented for use in a control laboratory. Using this conveyor set-up, students can experience how to program a practical example of a PLC devices used in industry together with an introduction to actuators and sensors. Reading actual sensors and using actuators via the inputs and outputs of a PLC to control a real life system will be far more effective than simulator alternatives.

Learning the relationship between a response variable (e.g., a quality characteristic) and a set of predictors (e.g., process variables) is of special importance in process modeling, prediction, and optimization. In many applications, not only is the number of these variables large but these variables are also high-dimensional (HD) (e.g., they are represented by waveform signals). This high dimensionality requires a systematic approach to both modeling the relationship between the variables and removing the noninformative input variables. This article proposes a functional regression method in which an HD response is estimated and predicted through a set of informative and noninformative HD covariates. For this purpose, the functional regression coefficients are expanded through a set of low-dimensional smooth basis functions. In order to estimate the low-dimensional set of parameters, a penalized loss function with both smoothing and group lasso penalties is defined. The block coordinate decent (BCD) method is employed to develop a computationally tractable algorithm for minimizing the loss function. Through simulations and case studies, the performance of the proposed method is evaluated and compared with benchmarks. The results illustrate the advantage of the proposed method over the benchmarks. Note to Practitioners —This article proposes a method for efficient and interpretable modeling of processes with high-dimensional (HD) data, such as waveform signals. Especially, the proposed method generates a regression model that predicts a function (e.g., a sensor’s readings over time) using several functional inputs. Existing functional regression techniques are mostly limited to a single functional input and are focused on profile data. In many applications, however, a large number of process variables are available for estimating an HD output, such as an image. This article addresses these problems by employing basis functions to reduce the dimension of functions and introducing specific penalties that removes noninformative inputs and improves computational efficiency. A model generated by the proposed approach can be used for process monitoring and optimization. Using simulation and case studies, the performance of the developed method is evaluated and compared with other methods under various scenarios. This can provide practitioners with useful guidelines for selecting an appropriate method for process modeling.

Software producing organizations attempt to improve their cost, schedule and quality performance by defining an organizational software process model. Educators try to teach their students the virtues of following such a process in software development and maintenance. But most software process models are proprietary so there are few readily available examples to help guide these efforts. This paper outlines the ‘Generic University of West Florida Maintenance Process (GUMP)’ which has been developed, used and refined over a period of almost two years in an educational setting. It describes the key maintenance process issues identified in this experience and the rationale for the solutions adopted in GUMP. As an academic tool, GUMP has greatly improved the learning experience in our project courses; two of our graduates have taken it as a starting point for developing a process for their employers. Full documentation for GUMP is publicly available on-line and may be copied and adapted as needed. GUMP is presented not as a ‘perfect’ process, but simply as a starting point that may be useful to either maintainers or educators as a point of reference for software process concepts.

Software producing organizations attempt to improve their cost, schedule and quality performance by defining an organizational software process model. Educators try to teach their students the virtues of following such a process in software development and maintenance. But most software process models are proprietary so there are few readily available examples to help guide these efforts. This paper outlines the ‘Generic University of West Florida Maintenance Process (GUMP)’ which has been developed, used and refined over a period of almost two years in an educational setting. It describes the key maintenance process issues identified in this experience and the rationale for the solutions adopted in GUMP. As an academic tool, GUMP has greatly improved the learning experience in our project courses; two of our graduates have taken it as a starting point for developing a process for their employers. Full documentation for GUMP is publicly available on-line and may be copied and adapted as needed. GUMP is presented not as a ‘perfect’ process, but simply as a starting point that may be useful to either maintainers or educators as a point of reference for software process concepts.

The emerging debate on the climate impact of food is expected to result in the carbon labelling of food in the future. As yet, consumer responses to carbon labels are not well researched. A real life experiment was developed to study consumer responses to new carbon labels for food. A ‘light’ and a ‘comprehensive’ carbon labelling regime were stepwise introduced into the food practice of ‘having lunch in a canteen’. The resulting shifts in the behaviour and attitudes of regular canteen visitors were measured with the use of both quantitative and qualitative methods. Despite their overall positive attitude towards climate change policies, the canteen visitors show clear resistance to some of the changes suggested, especially when new courses of action run counter to the dynamics of the existing routinised practice. Using practice theory as our reference, we discuss the role of information provision next to other policy instruments for facilitating behavioural changes towards low carbon food consumption.

Penalty kicks are analysed in the literature as ‘real life experiments’ for assessing the use of rational mixed strategies by professional players. However, each penalty kick cannot be considered a repetition of the same event because of the varying background conditions, in particular the heterogeneous ability of different players. Consequently, aggregate statistics over data sets composed of a large number of penalty kicks mediate the behaviour of the players in different games, and the properties of optimal mixed strategies cannot be tested directly because of aggregation bias. In this article, we model the heterogeneous ability of players. We then test the hypothesis that differently talented players randomize over different actions. To achieve this aim, we study a data set that collects penalties kicked during shoot-out series in the last editions of FIFA World Cup and UEFA Euro Cup (1994–2012) where kickers are categorized as specialists and non-specialists. The results support our theoretical predictions.

Part machining is a discrete manufacturing process. In order to evaluate the manufacturing process, an intelligent modeling method based on the first-order predicate logic is proposed. First, the basic predicate formula is defined according to the machining method, and the predicate and variables are illustrated in detail. Thus, the process representation is completed. Second, to construct the process model, the modeling element is put forward, which includes three nodes. Components of modeling element are, respectively, discussed, as well as the mapping relationship between modeling element and predicate. After the definition of modeling predicate formula, five basic inference rules are established. Consequently, the manufacturing process model is constructed. Third, on the basis of the process model, the process simulation is carried out to evaluate the manufacturing performances, such as the production efficiency, the utilization rate of machining equipment, the production bottleneck, etc. Finally, a case study is conducted to explain this modeling method.

The toilet of Venus is the subject of many paintings. Typically, Venus appears with a small mirror in which her face is visible. Observers tend to say that Venus is admiring herself in a mirror, even when the location of the mirror makes this impossible. We demonstrate that the Venus effect is not specific to paintings by showing that it occurs in real life (Experiment 1) and in photographs (Experiments 1-4). The original description of the effect implied that observers describe Venus as seeing in a mirror what they (the observers) see. We used different photographs to compare the responses when the person in front of the mirror could or could not see him or herself and when the image of his or her face was or was not visible to the observer. Observers tend to state that a person can see his or her own reflection when he or she appears near a mirror, whether or not his or her face is visible in the mirror. A task based on a top-down view of a room confirmed that people lack sensitivity to the role of the viewpoint (Experiment 5). We discuss these findings in relation to other evidence of difficulty in understanding what is visible in a mirror.

Habitat models are now broadly used in conservation planning on public lands. If implemented correctly, habitat modelling is a transparent and repeatable technique for describing and mapping biodiversity values, and its application in peri‐urban and agricultural landscape planning is likely to expand rapidly. Conservation planning in such landscapes must be robust to the scrutiny that arises when biodiversity constraints are placed on developers and private landholders. A standardized modelling and model evaluation method based on widely accepted techniques will improve the robustness of conservation plans. We review current habitat modelling and model evaluation methods and provide a habitat modelling case study in the New South Wales central coast region that we hope will serve as a methodological template for conservation planners. We make recommendations on modelling methods that are appropriate when presence‐absence and presence‐only survey data are available and provide methodological details and a website with data and training material for modellers. Our aim is to provide practical guidelines that preserve methodological rigour and result in defendable habitat models and maps. The case study was undertaken in a rapidly developing area with substantial biodiversity values under urbanization pressure. Habitat maps for seven priority fauna species were developed using logistic regression models of species‐habitat relationships and a bootstrapping methodology was used to evaluate model predictions. The modelled species were the koala, tiger quoll, squirrel glider, yellow‐bellied glider, masked owl, powerful owl and sooty owl. Models ranked sites adequately in terms of habitat suitability and provided predictions of sufficient reliability for the purpose of identifying preliminary conservation priority areas. However, they are subject to multiple uncertainties and should not be viewed as a completely accurate representation of the distribution of species habitat. We recommend the use of model prediction in an adaptive framework whereby models are iteratively updated and refined as new data become available.

DevOps is a new software engineering paradigm adopted by various software organizations to develop the quality software within time and budget. The implementation of DevOps practices is critical, and there are no guidelines to assess and improve the DevOps activities in software organizations. Hence, there is a need to develop a readiness model for DevOps (RMDevOps) with an aim to assist the practitioners for implementation of DevOps practices in software firms. To achieve the study objective, we conducted a systematic literature review (SLR) study to identify the critical challenges and associated best practices of DevOps. A total of 18 challenges and 73 best practices were identified from the 69 primary studies. The identified challenges and best practices were further evaluated by conducting a survey with industry practitioners. The RMDevOps was developed based on other well‐established models in software engineering domain, for example, software process improvement readiness model (SPIRM) and software outsourcing vendor readiness model (SOVRM). Finally, case studies were conducted with three different organizations with an aim to validate the developed model. The results show that the RMDevOps is effective to assess and improve the DevOps practices in software organizations.