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Register a new userInvestigating a Conceptual Construct for Software Context
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Added by
Stephen MacDonell
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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A growing number of empirical software engineering researchers suggest that a complementary focus on theory is required if the discipline is to mature. A first step in theory-building involves the establishment of suitable theoretical constructs. For researchers studying software projects, the lack of a theoretical construct for context is problematic for both experimentation and effort estimation. For experiments, insufficiently understood contextual factors confound results, and for estimation, unstated contextual factors affect estimation reliability. We have earlier proposed a framework that we suggest may be suitable as a construct for context i.e. represents a minimal, spanning set for the space of software contexts. The framework has six dimensions, described as Who, Where, What, When, How and Why. In this paper, we report the outcomes of a pilot study to test its suitability by categorising contextual factors from the software engineering literature into the framework. We found that one of the dimensions, Why, does not represent context, but rather is associated with objectives. We also identified some factors that do not clearly fit into the framework and require further investigation. Our contributions are the pursuing of a theoretical approach to understanding software context, the initial establishment and evaluation of a construct for context and the exposure of a lack of clarity of meaning in many contexts currently applied as factors for estimating project outcomes.
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In the domain of software engineering, our efforts as researchers to advise industry on which software practices might be applied most effectively are limited by our lack of evidence based information about the relationships between context and practice efficacy. In order to accumulate such evidence, a model for context is required. We are in the exploratory stage of evolving a model for context for situated software practices. In this paper, we overview the evolution of our proposed model. Our analysis has exposed a lack of clarity in the meanings of terms reported in the literature. Our base model dimensions are People, Place, Product and Process. Our contributions are a deepening of our understanding of how to scope contextual factors when considering software initiatives and the proposal of an initial theoretical construct for context. Study limitations relate to a possible subjectivity in the analysis and a restricted evaluation base. In the next stage in the research, we will collaborate with academics and practitioners to formally refine the model.
Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects (referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy using moving windows. The existence of the Bellwether was empirically proven based on six postulations. We apply statistical stratification and Markov chain methodology to select the Bellwether moving window. The resulting Bellwether moving window is used to predict the software effort of a new project. Empirical results show that Bellwether effect exist in chronological datasets with a set of exemplary and recently completed projects representing the Bellwether moving window. Result from this study has shown that the use of Bellwether moving window with the Gaussian weighting function significantly improve the prediction accuracy.
It is widely acknowledged by researchers and practitioners that software development methodologies are generally adapted to suit specific project contexts. Research into practices-as-implemented has been fragmented and has tended to focus either on the strength of adherence to a specific methodology or on how the efficacy of specific practices is affected by contextual factors. We submit the need for a more holistic, integrated approach to investigating context-related best practice. We propose a six-dimensional model of the problem-space, with dimensions organisational drivers (why), space and time (where), culture (who), product life-cycle stage (when), product constraints (what) and engagement constraints (how). We test our model by using it to describe and explain a reported implementation study. Our contributions are a novel approach to understanding situated software practices and a preliminary model for software contexts.
In 2006, Geoffrey Hinton proposed the concept of training Deep Neural Networks (DNNs) and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DNNs applied in SE. Few works to date focus on summarizing, classifying, and analyzing the application of deep learning techniques in SE. To fill this gap, we performed a survey to analyse the relevant studies published since 2006. We first provide an example to illustrate how deep learning techniques are used in SE. We then summarize and classify different deep learning techniques used in SE. We analyzed key optimization technologies used in these deep learning models, and finally describe a range of key research topics using DNNs in SE. Based on our findings, we present a set of current challenges remaining to be investigated and outline a proposed research road map highlighting key opportunities for future work.
AI-based systems are software systems with functionalities enabled by at least one AI component (e.g., for image- and speech-recognition, and autonomous driving). AI-based systems are becoming pervasive in society due to advances in AI. However, there is limited synthesized knowledge on Software Engineering (SE) approaches for building, operating, and maintaining AI-based systems. To collect and analyze state-of-the-art knowledge about SE for AI-based systems, we conducted a systematic mapping study. We considered 248 studies published between January 2010 and March 2020. SE for AI-based systems is an emerging research area, where more than 2/3 of the studies have been published since 2018. The most studied properties of AI-based systems are dependability and safety. We identified multiple SE approaches for AI-based systems, which we classified according to the SWEBOK areas. Studies related to software testing and software quality are very prevalent, while areas like software maintenance seem neglected. Data-related issues are the most recurrent challenges. Our results are valuable for: researchers, to quickly understand the state of the art and learn which topics need more research; practitioners, to learn about the approaches and challenges that SE entails for AI-based systems; and, educators, to bridge the gap among SE and AI in their curricula.
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