Towards Automated Performance Bug Identification in Python

Context: Software performance is a critical non-functional requirement, appearing in many fields such as mission critical applications, financial, and real time systems. In this work we focused on early detection of performance bugs; our software under study was a real time system used in the advertisement/marketing domain. Goal: Find a simple and easy to implement solution, predicting performance bugs. Method: We built several models using four machine learning methods, commonly used for defect prediction: C4.5 Decision Trees, Na\"{\i}ve Bayes, Bayesian Networks, and Logistic Regression. Results: Our empirical results show that a C4.5 model, using lines of code changed, file's age and size as explanatory variables, can be used to predict performance bugs (recall=0.73, accuracy=0.85, and precision=0.96). We show that reducing the number of changes delivered on a commit, can decrease the chance of performance bug injection. Conclusions: We believe that our approach can help practitioners to eliminate performance bugs early in the development cycle. Our results are also of interest to theoreticians, establishing a link between functional bugs and (non-functional) performance bugs, and explicitly showing that attributes used for prediction of functional bugs can be used for prediction of performance bugs

Understanding Consumer Behaviour to Reduce Environmental Impacts through Sustainable Product Design

The use phase of the lifecycle of electrical products has a significant environmental impact, mainly determined by the consumer’s behaviour. Many consumers do not make the link between their daily consumption behaviour in the household and environmental problems such as climate change. In the 21st century, the residential sector, together with transport and industry, is one of the largest man-made contributors in the UK to climate change. It is argued that technological innovations, current eco-efficient products and consumer education have been ineffective in creating the long term radical behavioural change needed to reduce the impact of product use. Products, as the interface between consumers and consumption activities, have the potential to influence the way in which consumption occurs. In the sustainable design field however, designer responsibility traditionally considers raw material selection and product disposal. There is limited work that addresses the environmental impacts relating directly to use behaviour of the product. This paper illustrates that user behaviour studies can be the preliminary step for designers to improve energy efficiency of products. A single product type, household cold appliance, was chosen as a case to explore the capacity of designer-conducted user study to identify unsustainable aspects of product use. Adopting a user-centred approach, two pilot studies were used to gain an insight into domestic fridge and freezer use in the UK. Qualitative ethnographical research methods were employed to investigate the daily practices and “real” needs of user as well as the connection between the knowledge, attitudes, intention and actual action. The design suggestions drawn from the user behaviour analysis provide examples of how energy impact level of the interaction with the product can be reduced through design. Keywords: User-Centred Research; Sustainable Product Design; Changing Consumer Behaviour; Design Research; Household Energy Consumption; Household Cold Appliance.</p

Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine

Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)

Definition, implementation and validation of energy code smells: an exploratory study on an embedded system

Optimizing software in terms of energy efficiency is one of the challenges that both research and industry will have to face in the next few years.We consider energy efficiency as a software product quality characteristic, to be improved through the refactoring of appropriate code pattern: the aim of this work is identifying those code patterns, hereby defined as Energy Code Smells, that might increase the impact of software over power consumption. For our purposes, we perform an experiment consisting in the execution of several code patterns on an embedded system. These code patterns are executed in two versions: the first one contains a code issue that could negatively impact power consumption, the other one is refactored removing the issue. We measure the power consumption of the embedded device during the execution of each code pattern. We also track the execution time to investigate whether Energy Code Smells are also Performance Smells. Our results show that some Energy Code Smells actually have an impact over power consumption in the magnitude order of micro Watts. Moreover, those Smells did not introduce a performance decreas

Design Performance Measurement in the Construction Sector: A Pilot Study

This paper examines the role and deployment of design performance measurements (DPMs) in the construction industry, focusing on the consulting engineering sector, the design 'heart' of construction. Compared with manufacturing, there has been very little research on the use of DPMs in construction, and firms often struggle to find appropriate performance indicators. Using results from structured questionnaires, the paper shows that the few DPMs which do exist focus mainly on cost. Other measures are needed to address quality, innovative performance and client satisfaction. In contrast to manufacturing, DPMs in construction also need to address the project-based, multi-firm and non-routine nature of construction design, as well as the separation of design from manufacturing, build and operation. Interviews and workshops with industrialists were used to identify recent DPM practices in construction and combine these with lessons from other sectors. The resulting DPM tools provide guidance on how to: (a) integrate design into wider business processes in construction; (b) identify key design indicators, at both project and firm level; and (c) use DPMs to provide a balanced scorecard for design performance.performance indicators, design integration, design indicators, construction industry

Naming the Pain in Requirements Engineering: A Design for a Global Family of Surveys and First Results from Germany

For many years, we have observed industry struggling in defining a high quality requirements engineering (RE) and researchers trying to understand industrial expectations and problems. Although we are investigating the discipline with a plethora of empirical studies, they still do not allow for empirical generalisations. To lay an empirical and externally valid foundation about the state of the practice in RE, we aim at a series of open and reproducible surveys that allow us to steer future research in a problem-driven manner. We designed a globally distributed family of surveys in joint collaborations with different researchers and completed the first run in Germany. The instrument is based on a theory in the form of a set of hypotheses inferred from our experiences and available studies. We test each hypothesis in our theory and identify further candidates to extend the theory by correlation and Grounded Theory analysis. In this article, we report on the design of the family of surveys, its underlying theory, and the full results obtained from Germany with participants from 58 companies. The results reveal, for example, a tendency to improve RE via internally defined qualitative methods rather than relying on normative approaches like CMMI. We also discovered various RE problems that are statistically significant in practice. For instance, we could corroborate communication flaws or moving targets as problems in practice. Our results are not yet fully representative but already give first insights into current practices and problems in RE, and they allow us to draw lessons learnt for future replications. Our results obtained from this first run in Germany make us confident that the survey design and instrument are well-suited to be replicated and, thereby, to create a generalisable empirical basis of RE in practice