Industrially Applicable System Regression Test Prioritization in Production Automation

When changes are performed on an automated production system (aPS), new faults can be accidentally introduced in the system, which are called regressions. A common method for finding these faults is regression testing. In most cases, this regression testing process is performed under high time pressure and on-site in a very uncomfortable environment. Until now, there is no automated support for finding and prioritizing system test cases regarding the fully integrated aPS that are suitable for finding regressions. Thus, the testing technician has to rely on personal intuition and experience, possibly choosing an inappropriate order of test cases, finding regressions at a very late stage of the test run. Using a suitable prioritization, this iterative process of finding and fixing regressions can be streamlined and a lot of time can be saved by executing test cases likely to identify new regressions earlier. Thus, an approach is presented in this paper that uses previously acquired runtime data from past test executions and performs a change identification and impact analysis to prioritize test cases that have a high probability to unveil regressions caused by side effects of a system change. The approach was developed in cooperation with reputable industrial partners active in the field of aPS engineering, ensuring a development in line with industrial requirements. An industrial case study and an expert evaluation were performed, showing promising results.Comment: 13 pages, https://ieeexplore.ieee.org/abstract/document/8320514

The institutional character of computerized information systems

We examine how important social and technical choices become part of the history of a computer-based information system (CB/SJ and embedded in the social structure which supports its development and use. These elements of a CBIS can be organized in specific ways to enhance its usability and performance. Paradoxically, they can also constrain future implementations and post-implementations.We argue that CBIS developed from complex, interdependent social and technical choices should be conceptualized in terms of their institutional characteristics, as well as their information-processing characteristics. The social system which supports the development and operation of a CBIS is one major element whose institutional characteristics can effectively support routine activities while impeding substantial innovation. Characterizing CBIS as institutions is important for several reasons: (1) the usability of CBIS is more critical than the abstract information-processing capabilities of the underlying technology; (2) CBIS that are well-used and have stable social structures are more difficult to replace than those with less developed social structures and fewer participants; (3) CBIS vary from one social setting to another according to the ways in which they are organized and embedded in organized social systems. These ideas are illustrated with the case study of a failed attempt to convert a complex inventory control system in a medium-sized manufacturing firm

A Survey on Software Testing Techniques using Genetic Algorithm

The overall aim of the software industry is to ensure delivery of high quality software to the end user. To ensure high quality software, it is required to test software. Testing ensures that software meets user specifications and requirements. However, the field of software testing has a number of underlying issues like effective generation of test cases, prioritisation of test cases etc which need to be tackled. These issues demand on effort, time and cost of the testing. Different techniques and methodologies have been proposed for taking care of these issues. Use of evolutionary algorithms for automatic test generation has been an area of interest for many researchers. Genetic Algorithm (GA) is one such form of evolutionary algorithms. In this research paper, we present a survey of GA approach for addressing the various issues encountered during software testing.Comment: 13 Page

Mbed OS regression test selection and optimization

Abstract. Testing is a fundamental building block in the identification of bugs, errors and defects in both hardware and software. Effective testing of large projects requires automated testing, test selection and test optimization. Using CI (Continuous Integration) tools, and test selection and optimization techniques reduce development time and increase productivity. The prioritization, selection and minimization of tests are well-known problems in software testing. Arm Mbed OS is a free, open-source embedded operating system designed specifically for the “things” in the IoT (Internet of Things). This thesis researches regression test selection (RTS) and optimization techniques (RTO). The main focus of the thesis is to develop a set of effective automated safe RTS (mbedRTS) and RTO (mbedRTO) techniques for Mbed OS pull request (PR) testing. This thesis refers to the set of developed techniques as Mbed OS regression test techniques (MbedRTT), also known as Mbed OS Smart Tester. The empirical analysis of the researched, and developed MbedRTT techniques show promising results. Several developed MbedRTT techniques have already been adopted in Mbed OS Jenkins CI.Mbed OS -regressiotestien valinta ja optimointi. Tiivistelmä. Testaus on olennainen tekijä vikojen ja virheiden tunnistamisessa sekä ohjelmistossa että laitteistossa. Isojen projektien tehokas testaaminen vaatii automaattista testausta, testien valintaa ja testien optimointia. Jatkuvan integraation (engl. continuous integration) työkalut, testien valintatekniikat ja testien optimointitekniikat lyhentävät kehitykseen kuluvaa aikaa ja kasvattavat tuottavuutta. Testien priorisointi, valinta ja minimointi ovat tunnettuja ongelmia ohjelmistotestauksessa. Arm Mbed OS on ilmainen avoimen lähdekoodin sulautettu käyttöjärjestelmä, joka on tarkoitettu erityisesti “asioille” asioiden Internetissä (engl. Internet of Things). Tässä työssä tutkitaan regressiotestauksen valinta- ja optimointimenetelmiä. Tämän työn päätehtävä on kehittää tehokkaita ja turvallisia valinta- (mbedRTS) ja optimointimenetelmiä (mbedRTO) Mbed OS pull request:ien regressiotestaukseen. Mbed OS -regressiotestausmenetelmillä (MbedRTT) viitataan tässä työssä kehitettyihin regressiotestausmenetelmiin, jotka tunnetaan myös nimellä Mbed OS älykäs testaaja (engl. Mbed OS Smart Tester). Tutkittujen ja kehitettyjen MbedRTT-tekniikoiden empiirisen analyysin tulos näyttää lupaavalta. Mbed OS Jenkins CI:ssä on jo otettu käyttöön useita kehitettyjä MbedRTT-tekniikoita

Impact estimation: IT priority decisions

Given resource constraints, prioritization is a fundamental process within systems engineering to decide what to implement. However, there is little guidance about this process and existing IT prioritization methods have several problems, including failing to adequately cater for stakeholder value. In response to these issues, this research proposes an extension to an existing prioritization method, Impact Estimation (IE) to create Value Impact Estimation (VIE). VIE extends IE to cater for multiple stakeholder viewpoints and to move towards better capture of explicit stakeholder value. The use of metrics offers VIE the means of expressing stakeholder value that relates directly to real world data and so is informative to stakeholders and decision makers. Having been derived from prioritization factors found in the literature, stakeholder value has been developed into a multi-dimensional, composite concept, associated with other fundamental system concepts: objectives, requirements, designs, increment plans, increment deliverables and system contexts. VIE supports the prioritization process by showing where the stakeholder value resides for the proposed system changes. The prioritization method was proven to work by exposing it to three live projects, which served as case studies to this research. The use of the extended prioritization method was seen as very beneficial. Based on the three case studies, it is possible to say that the method produces two major benefits: the calculation of the stakeholder value to cost ratios (a form of ROI) and the system understanding gained through creating the VIE table

How do software architects consider non-functional requirements: an exploratory study

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Dealing with non-functional requirements (NFRs) has posed a challenge onto software engineers for many years. Over the years, many methods and techniques have been proposed to improve their elicitation, documentation, and validation. Knowing more about the state of the practice on these topics may benefit both practitioners' and researchers' daily work. A few empirical studies have been conducted in the past, but none under the perspective of software architects, in spite of the great influence that NFRs have on daily architects' practices. This paper presents some of the findings of an empirical study based on 13 interviews with software architects. It addresses questions such as: who decides the NFRs, what types of NFRs matter to architects, how are NFRs documented, and how are NFRs validated. The results are contextualized with existing previous work.Peer ReviewedPostprint (author’s final draft

DeepPR: Progressive Recovery for Interdependent VNFs with Deep Reinforcement Learning

The increasing reliance upon cloud services entails more flexible networks that are realized by virtualized network equipment and functions. When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted in a progressive way due to limited repair resources. The prioritization of network equipment in the recovery phase influences the interim computation and communication capability of systems, since the systems are operated under partial functionality. Hence, finding the best recovery order is a critical problem, which is further complicated by virtualization due to dependency among network nodes and layers. This paper deals with a progressive recovery problem under limited resources in networks with VNFs, where some dependent network layers exist. We prove the NP-hardness of the progressive recovery problem and approach the optimum solution by introducing DeepPR, a progressive recovery technique based on Deep Reinforcement Learning (Deep RL). Our simulation results indicate that DeepPR can achieve the near-optimal solutions in certain networks and is more robust to adversarial failures, compared to a baseline heuristic algorithm.Comment: Technical Report, 12 page