Software Changes Research Articles

A MACHINE LEARNING BASED APPROACH FOR DETECTING NON-DETERMINISTIC TESTS AND ITS ANALYSIS IN MOBILE APPLICATION TESTING

Hundreds of different mobile devices are on the market, produced by different vendors, and with different software features and hardware components. Mobile applications, while running on different devices, may behave differently due to variations in the hardware or O.S. components. Since mobile applications are expected to be deployed and executed on diverse mobile platforms, they must be validated on different mobile platforms and devices. Due to the peculiarities of mobile application development, there is a need for a quality assurance approach that focuses on its challenges. Moreover, mobile test executions take a long time because all the tests were executed in different environments and developers had to create complex tear down procedures. Such procedures were lengthy and far from perfect, leading to unpredictable failures. Regression testing is a crucial part of Mobile app development and it checks that software changes do not break existing functionality. An important assumption of regression testing is that test outcomes are deterministic and tests are expected to either always pass or always fail for the same code under test. But unfortunately, in real projects multiple release cycles, some tests— often called flaky tests—have non-deterministic outcomes. These tests undermine the regression testing cycle as they make it difficult to rely on test results. These results significantly reduced the trust in the tests and thus undermined the whole mobile app test automation effort. We trained machine learning classifiers separately on each test result dataset and compared performance across datasets. The proposed model predicts result types as Non-Deterministic or Deterministic tests from the regression suite results executed in various release cycles.

How to keep your systems records safe

The success of an institution depends on its ability to acquire accurate and timely data about its operations, to manage and use this data effectively. Computer laboratory record management plays a vital role in the organization and maintenance of computers in an institution. In existing system of Mehran University of Engineering and Technology, Jamshoro lab assistants are responsible to view the current status and configuration of individual computers in LAN based computer labs. The other and very important problem which lab assistants face is that they don’t know automatically which hardware and software changes are being done on any system. In this paper a Computer Laboratory Record Management System (CLRMS) is proposed to avoid such kind of problems. CLRMS is a network based Software which automatically keeps all the information of system including hardware and software. This software secures the records of system configuration for future information reporting, user management and security management. It also updates system database if any hardware is removed or any software changes occur via automatically retrieval method. CLRMS interfaces are easily understandable hence user friendly. It generates reports for both software and hardware. It shows complete structured configuration of a computer. It will keep records that which type of user was log on the system and what tasks he has performed like installation information of software, updates of software, version and vendor information.

The Marburg-Münster Affective Disorders Cohort Study (MACS): A quality assurance protocol for MR neuroimaging data

Large, longitudinal, multi-center MR neuroimaging studies require comprehensive quality assurance (QA) protocols for assessing the general quality of the compiled data, indicating potential malfunctions in the scanning equipment, and evaluating inter-site differences that need to be accounted for in subsequent analyses.We describe the implementation of a QA protocol for functional magnet resonance imaging (fMRI) data based on the regular measurement of an MRI phantom and an extensive variety of currently published QA statistics. The protocol is implemented in the MACS (Marburg-Münster Affective Disorders Cohort Study, http://for2107.de/), a two-center research consortium studying the neurobiological foundations of affective disorders. Between February 2015 and October 2016, 1214 phantom measurements have been acquired using a standard fMRI protocol. Using 444 healthy control subjects which have been measured between 2014 and 2016 in the cohort, we investigate the extent of between-site differences in contrast to the dependence on subject-specific covariates (age and sex) for structural MRI, fMRI, and diffusion tensor imaging (DTI) data.We show that most of the presented QA statistics differ severely not only between the two scanners used for the cohort but also between experimental settings (e.g. hardware and software changes), demonstrate that some of these statistics depend on external variables (e.g. time of day, temperature), highlight their strong dependence on proper handling of the MRI phantom, and show how the use of a phantom holder may balance this dependence. Site effects, however, do not only exist for the phantom data, but also for human MRI data. Using T1-weighted structural images, we show that total intracranial (TIV), grey matter (GMV), and white matter (WMV) volumes significantly differ between the MR scanners, showing large effect sizes. Voxel-based morphometry (VBM) analyses show that these structural differences observed between scanners are most pronounced in the bilateral basal ganglia, thalamus, and posterior regions. Using DTI data, we also show that fractional anisotropy (FA) differs between sites in almost all regions assessed. When pooling data from multiple centers, our data show that it is a necessity to account not only for inter-site differences but also for hardware and software changes of the scanning equipment. Also, the strong dependence of the QA statistics on the reliable placement of the MRI phantom shows that the use of a phantom holder is recommended to reduce the variance of the QA statistics and thus to increase the probability of detecting potential scanner malfunctions.

Автоматизация процедуры идентификации объектов на основе логики предикатов

One of the defining tasks in automating the processing of current information obtained in the process of object research is the possibility of assigning it to specific objects located in the database (DB) of the information system. Such a task can be represented as a task of identifying the objects of analysis (monitoring) on current data. Initial information about a particular facility during the research can come from different sources and be incomplete and inaccurate. Due to identification, received data for each object are accumulated in the database for further automated analysis. But in a number of cases, the current information contains not specific names of the research objects of interest, but only some signs of their functioning: location, operating time, numerical characteristics of objects, etc. In such conditions, the identification of objects can occur on the basis of similarity of semantic features contained in the current information and attributes which have been already accumulated by the objects of research in the database. A distinctive feature of the subject area in which the analysis of objects is made is its variability; therefore, with the passage of time, the attributes serving to identify the objects may change. The addition of new features will result in a change in application software and will require the involvement of developers, which leads to additional time costs and decrease in the speed of procedure for identifying objects. This reason leads to the need to create a new approach to the automation of object monitoring, based on such requirements as high quality of the decisions made and ability to quickly change the logic of identification in the process of the system. The paper proposes the possibility of using predicate logic formulas, with the aim of making new information in the database more quickly and assigning it to a specific object, which makes it possible to develop the fastest solution.

FUNNEL: Assessing Software Changes in Web-Based Services

The detection of performance changes in software change roll-outs in Internet-based services is crucial for an operations team, because it allows timely roll-back of a software change when performance degrades unexpectedly. However, it is infeasible to manually investigate millions of performance measurements of many roll-outs. In this paper, we present an automated tool, FUNNEL, for rapid and robust impact assessment of software changes in large Internet-based services. FUNNEL automatically collects the related performance measurements for each software change. To detect significant performance behavior changes, FUNNEL adopts singular spectrum transform (SST) algorithm as the core algorithm, uses various techniques to improve its robustness and reduce its computational cost, and applies a difference-in-difference (DiD) method to differentiate the true causality from the random correlations between the performance change and the software change. Evaluation through historical data in real-word services shows that FUNNEL achieves accuracy of more than 99.7 percent. Compared with previous methods, FUNNEL’s detection delay is 38.02 to 64.99 percent shorter, and its computation speed is 4.59-7,098 times faster. In real deployment, FUNNEL achieves a 98.21 percent precision, high robustness, fast detection speed, and shows its capability in detecting unexpected behavior changes.

Characterization and Detection of Tail Energy Bugs in Smartphones

Smartphones are the most ubiquitous and popular hand-held devices because of their rich set of features and wide variety of services that require frequent battery recharging. In smartphones, most energy issues are due to energy bugs (ebugs). These ebugs are said to be exist when smartphone software applications (apps) consume more power than expected while executing, or they continue to consume energy even after these apps are closed or terminated. In this research, we introduce the concept of application tail energy bugs (ATEBs) and provide an operational definition for it. Then, we discuss about the main potential causes of ATEBs and the user actions that can trigger them. To provide a proof of concept, we conduct experiments using real Android apps. To identify all the scenarios that can cause ATEBs, we develop a testing app and perform 32 experiments. The main goal of the experiments is to check the behavior of app components, such as activities and services in the presence/absence of four different types of wakelocks. Then, we discuss the relationship between software changes and energy consumption by tracing wakelocks that keep the device awake and services that might be engaging the CPU. The power consumption of the app is measured using the monsoon power meter. Because power meters are not often available to software developers, we design a tool to detect ATEBs. This tool utilizes Android debug bridge (adb) commands to extract system-related information. The tool effectiveness is evaluated using five Android apps.

English Grammar on 2013 Curriculum: The Development of Game Based Learning Multimedia

This Research is aimed to find out the effectiveness of game based learning multimedia in order to gain student learning outcomes on English subject with the topic of grammar in the classroom. The research development of multimedia game based learning using the procedure of Raiser and Demsey consisting of 5 stages namely: 1) Analysis which covers needs and theoretical analysis, 2) Design which includes Core and basic competence’s identification, material’s selection and submission, flowchart and story board arrangement, 3) Develop that contains production, media and material’s experts validation and students’ responses and product’s trial after the revision done. 4) Implement that covers the final product’s application towards the experiment class, 5) Evaluate becoming the last stage of all by having a test for both classes which are control and experiment class to see whether is there any differences between the class applying the multimedia game based learning and not. The trial of product development tested had passed several processes covering; the review from media’s expert which includes software changes and visual communication, the review from content’s expert covers material and instructional design, students’ responses towards products’ usage and attractiveness, small group and big group test trial. The result showed that the students at the control class taught by conventional way as in speech has less motivation and lower score compared with the experiment class applying interactive multimedia game based learning. There is an interaction between learning media and learning motivation those effects on students’ learning achievement especially in English grammar. Thus, it can be said that the result of the development of multimedia game based learning is effective to improve students learning outcomes.

Interfacing Complex Laboratory Instruments during a Change to Epic Beaker

Background: Implementing a laboratory-developed test sometimes requires incorporating an unconventional device into the laboratory information system (LIS) and customizing an interface to reduce transcription error and improve turnaround time. Such a custom interface is a necessity for complicated high-volume tests such as 25-OH Vitamin D by liquid chromatography-tandem mass spectrometry (LC-MS/MS) when there is no vendor-or LIS-supplied interface available. Here, we describe our work and experience interfacing a API 5000 LC-MS/MS instrument with our newly implemented LIS, Epic Beaker, using a combination of in-house scripting software and a middleware vendor, Data Innovations. Materials and Methods: For input interfacing, custom scripting software was developed to transcribe batched order lists generated by Epic into files usable by the instrument software, Analyst®. For output interfacing, results from the LC-MS/MS system were fed to a unidirectional instrument driver made by Data Innovations and selected data were transferred to the LIS. Results: Creation and validation of a new driver by Data Innovations took approximately 6 months. The interface was adopted for 25-OH Vitamin D and testosterone testing during periods of increasing test volume (4.5-fold over 8 years and 1.25-fold over 5 years). The amount of time spent reporting 25-OH Vitamin D results decreased 82% per order resulting in a savings of 1370 technician work hours and the amount of time spent reporting testosterone results decreased 75% per order resulting in a savings of 400 technician work hours. Conclusions: A mixed model using custom scripting and curated commercial middleware serve as a durable interface solution for laboratory instrumentation such as an LC-MS/MS and are flexible to future changes in instrument software, networking protocols, and the scope of LISs and work area managers.

Mining Variable-Method Correlation for Change Impact Analysis

Software change impact analysis (CIA) is a key technique to identify the potential ripple effects of the changes to software. Coarse-grained CIA techniques such as file, class and method level techniques often gain less precise change impacts, which are difficult for practical use. Fine-grained CIA techniques, such as slicing, can be used to gain more precise change impacts, but need more time and space cost. In this paper, by combining the features of the coarse-grained technique and the fine-grained technique, a variable-method (VM) correlation-based CIA technique called VM-CIA is proposed to improve the precision of static CIA. First, the VM-CIA technique uses the abstract syntax tree (AST) of program to construct a novel intermediate representation called variable and method triple (VMT), which is used to analyze the correlation between the variables and methods. Second, the VM-CIA technique proposes the single-change impact analysis algorithm and multi-change impact analysis algorithm to compute the impact set based on the VMT representation. In addition, the VM-CIA technique can get a sorted impact set which is more accurate than the existing CIA techniques. The empirical results show that the VM-CIA technique can greatly improve the precision (19%) over traditional the CIA techniques, while at the cost of a little recall (5%). Moreover, the empirical studies also show that the VM-CIA technique predicts a ranked list of potential impact results according to the distance measure, which can greatly facilitate the practical use.

The Challenges of Regulating Diffuse Agricultural Pollution to Improve Water Quality

Worldwide, the cumulative effects of diffuse pollution arising from a range of human activities are diminishing the quality and ecosystem capacity of lakes, rivers, estuaries, and oceans. Devising effective ways to regulate the causes and effects of diffuse pollution is a fraught legal, political, policy, and management challenge given the difficulties in identifying and measuring who is responsible for what, where, and when. In 2011, under its Resource Management Act, 1991, the South Pacific nation of New Zealand introduced national policy to arrest diffuse pollution with a requirement for local government to institute enforceable water quality and quantity limits on all freshwater bodies. The blueprint for these national freshwater policy reforms comes from its South Island region of Canterbury. Canterbury's regional council has adopted a catchment load approach whereby an overarching limit on nutrient losses from agricultural land is calculated and linked to land use rules to control property-scale agricultural activities. With a focus on the Canterbury region, this case study examines two approaches to establishing a catchment load for diffuse nutrient pollution to link to legal provisions in its regional plan. One is based on a river's nutrient concentrations and the other relies on predictive modelling. The case study opens important questions about measuring and regulating diffuse pollution and the difficulties faced by policy-makers and regulators in linking numbers to legally binding compliance and enforcement mechanisms, e.g. how to account for lag effects when establishing ‘in-stream’ limits and how to address changes in software when relying on ‘modelled’ limits?

Analysing change profiles of open source software projects using burst detection

Software evolution refers to the phenomenon of continuous software change and growth after its initial development. A version control system records all information about these changes. Several research studies in the past have studied the historical records of changes of open source software (OSS) projects and found them useful for understanding the software evolution process. However, most of them investigate the distributions of changes types, change size, and change effort in an isolated manner. There is no work, to the best of our knowledge, which takes a combined view of various dimensions of a change. This study examines the change activity in 106 OSS projects from three points of view: change purpose (type), change size, and change effort. The common patterns in change type, change size, and change effort are highlighted using the burst detection technique. The burst detection technique helps in identifying the peaks in the time series and compares them with the peaks of other time series. The results indicate that the change-type activity of OSS projects is significantly related with change effort, and change size for high and moderate-activity clusters. Though for low-activity cluster, this commonality of patterns is not there for all types of changes.

English

This study aims to analyse the potential organizational impacts related to introduction and implementation of new managerial concepts methods and tools in Italian entrepreneurship associations using direct observation through daily presence as methodology in the structure. The results revealed that although the context of entrepreneurial associations assumes a certain degree of proactivity and dynamism in dealing with change, those who work there still cannot take decisive steps towards innovation. The analysed structure is strongly traditional, conservative, and unwilling to accept changes in software, roles and responsibilities, with the resulting difficulties being operating in a context that is increasingly turbulent. As the study is based on a case study, the conclusions achieved can be limited to the specific context examined. Investigating how the society can face the necessary change in skills and capabilities will make clear if the traditional stativity of the association quoted in the literature have been confirmed or not and, no less important, how such changes are perceived and managed by the structure. From the direct observation of reality, ideas and suggestions on how to handle the criticalities, therefore, favorability and optimization of the association's evolution could be achieved. This research thus showed the possibility of making a significant change that is developed and shared by the structure. In addition, the fact that these applications are studied in a rarely analysed and unconventional context, such as that of entrepreneurial associations, strengthens the originality of the study.   Key words: Entrepreneurial association, business intelligence, change management.

Longitudinal Neuroimaging Analysis in Mild-Moderate Alzheimer's Disease Patients Treated with Plasma Exchange with 5% Human Albumin.

Background:Recently, modifications of Aβ1-42 levels in CSF and plasma associated with improvement in memory and language functions have been observed in patients with mild-moderate Alzheimer’s disease (AD) treated with plasma exchange (PE) with albumin replacement.Objective:To detect structural and functional brain changes in PE-treated AD patients as part of a Phase II clinical trial.Methods:Patients received between 3 and 18 PE with albumin (Albutein® 5%, Grifols) or sham-PE (controls) for 21 weeks (divided in one intensive and two maintenance periods) followed by 6-month follow-up. Brain perfusion assessed by SPECT scans using an automated software (NeuroGam®) and brain structural changes assessed by MRI were performed at weeks 0 (baseline), 21, and 44 (with additional SPECT at weeks 9 and 33). Statistical parametric mapping (voxel-based analysis, SPM) and Z-scores calculations were applied to investigate changes to baseline.Results:42 patients were recruited (39 evaluable; 37 analyzed: 18 PE-treated; 19 controls). There was a trend toward decreasing hippocampi and total intracranial volume for both patient groups during the study (p < 0.05). After six months, PE-treated patients had less cerebral perfusion loss than controls in frontal, temporal, and parietal areas, and perfusion stabilization in Brodmann area BA38-R during the PE-treatment period (p < 0.05). SPM analysis showed stabilization or absence of progression of perfusion loss in PE-treated patients until week 21, not observed in controls.Conclusions:Mild-moderate AD patients showed decreased brain volume and impairment of brain perfusion as expected for the progression of the disease. PE-treatment with albumin replacement favored the stabilization of perfusion.

An empirical study on the interplay between semantic coupling and co-change of software classes

Software systems continuously evolve to accommodate new features and interoperability relationships between artifacts point to increasingly relevant software change impacts. During maintenance, developers must ensure that related entities are updated to be consistent with these changes. Studies in the static change impact analysis domain have identified that a combination of source code and lexical information outperforms using each one when adopted independently. However, the extraction of lexical information and the measure of how loosely or closely related two software artifacts are, considering the semantic information embedded in their comments and identifiers has been carried out using somewhat complex information retrieval (IR) techniques. The interplay between software semantic and change relationship strengths has also not been extensively studied. This work aims to fill both gaps by comparing the effectiveness of measuring semantic coupling of OO software classes using (i) simple identifier based techniques and (ii) the word corpora of the entire classes in a software system. Afterwards, we empirically investigate the interplay between semantic and change coupling. The empirical results show that: (1) identifier based methods have more computational efficiency but cannot always be used interchangeably with corpora-based methods of computing semantic coupling of classes and (2) there is no correlation between semantic and change coupling. Furthermore we found that (3) there is a directional relationship between the two, as over 70% of the semantic dependencies are also linked by change coupling but not vice versa.

ChangeLocator: locate crash-inducing changes based on crash reports

Software crashes are severe manifestations of software bugs. Debugging crashing bugs is tedious and time-consuming. Understanding software changes that induce a crashing bug can provide useful contextual information for bug fixing and is highly demanded by developers. Locating the bug inducing changes is also useful for automatic program repair, since it narrows down the root causes and reduces the search space of bug fix location. However, currently there are no systematic studies on locating the software changes to a source code repository that induce a crashing bug reflected by a bucket of crash reports. To tackle this problem, we first conducted an empirical study on characterizing the bug inducing changes for crashing bugs (denoted as crash-inducing changes). We also propose ChangeLocator, a method to automatically locate crash-inducing changes for a given bucket of crash reports. We base our approach on a learning model that uses features originated from our empirical study and train the model using the data from the historical fixed crashes. We evaluated ChangeLocator with six release versions of Netbeans project. The results show that it can locate the crash-inducing changes for 44.7%, 68.5%, and 74.5% of the bugs by examining only top 1, 5 and 10 changes in the recommended list, respectively. It significantly outperforms the existing state-of-the-art approach.

Applying Information Measure for Predicting Release Time of Open Source Software

Open Source Software (OSS) is updated regularly to meet the requirements posed by the customers. The source code of OSS undergoes frequent change to diffuse new features and update existing features in the system, providing a user friendly interface. The source code changes for fixing bugs and meeting user end requirements again affects the complexity of the code change and creates bugs in the software which are accountable to the next release of software. In this paper, the complexity of code changes in various Bugzilla open source software releases, from version 2.0 on 19th Sep, 1998, to 5.0.1 on 10th Sep, 2015, bugs in each software version release, and the time of release of each software version are considered, and the data used to predict the next release time. The Shannon entropy measure is used to quantify the code change process in terms of entropy for each software release. Observed code changes are utilized to quantify them into entropy units and are further used to predict the next release time. A neural network-based regression model is used to predict the next release time. The performance is compared with the R measure calculated using the multi linear regression model, and a goodness of fit curve is produced.

Automatic rule installation in case of policy change in software defined networks

Software Defined Networking (SDN) has emerged recently as a new network architecture. It implements both control and management planes at centralized controller and data plane at forwarding devices. Therefore, SDN helps to simplify network management and improves network programmability. Changes in network policies occur frequently by making modifications at controller. However, in existing approaches, the rules installed at switches before policy change at controller are not modified. This can cause violation of network policy by packets. To address this problem, this paper presents a new approach that stores the rules generated at controller. After detecting the change in policy, the proposed approach finds the rules that will be affected by policy change by examining stored rules at controller. Then the affected rules are removed from the forwarding devices. Simulation results reveal that our proposed approach provides less packets violation ratio and normalized traffic overhead as compared to existing approach. Therefore, the proposed approach increases network performance and efficiency.

High prevalence of urine tampering in an office-based opioid treatment practice detected by evaluating the norbuprenorphine to buprenorphine ratio

The prevalence of urine tampering within office-based opioid treatment (OBOT) is not currently known. This study was a cross-sectional analysis of an OBOT practice in New York City that experienced both a change in provider and a change in electronic medical record software. At that time, every patient in the practice received a urine drug test for “quantitative buprenorphine metabolites.” MethodsOutcomes of the first three urine drug tests were tabulated and analyzed with specific attention to the frequency of buprenorphine-positive (bup+), norbuprenorphine-negative (norbup−) samples, a pattern consistent with urine tampering. ResultsOn the first sample 6/33 (18%) of patients submitted bup+/norbup− samples, and an additional 3 patients submitted bup+/norbup− samples on subsequent urine tests. Retention to the end of the study period among patients with bup+/norbup− samples was 33%, while in those with bup+/norbup+samples it was 96%. A scatter plot of norbuprenorphine vs. buprenorphine levels estimated that a ratio of <0.2 indicated tampering. ConclusionTesting for buprenorphine metabolites yields valuable clinical information. The prevalence of a result pattern consistent with tampering by “urine spiking,” the addition of unconsumed buprenorphine into the urine sample, may be higher than previous estimates. Previous lower cutoffs of the norbuprenorphine:buprenorphine metabolic ratio may miss a substantial proportion of these likely tampered samples.

Technical note: The US Dobson station network data record prior to 2015, re-evaluation of NDACC and WOUDC archived records with WinDobson processing software

Abstract. The United States government has operated Dobson ozone spectrophotometers at various sites, starting during the International Geophysical Year (1 July 1957 to 31 December 1958). A network of stations for long-term monitoring of the total column content (thickness of the ozone layer) of the atmosphere was established in the early 1960s and eventually grew to 16 stations, 14 of which are still operational and submit data to the United States of America's National Oceanic and Atmospheric Administration (NOAA). Seven of these sites are also part of the Network for the Detection of Atmospheric Composition Change (NDACC), an organization that maintains its own data archive. Due to recent changes in data processing software the entire dataset was re-evaluated for possible changes. To evaluate and minimize potential changes caused by the new processing software, the reprocessed data record was compared to the original data record archived in the World Ozone and UV Data Center (WOUDC) in Toronto, Canada. The history of the observations at the individual stations, the instruments used for the NOAA network monitoring at the station, the method for reducing zenith-sky observations to total ozone, and calibration procedures were re-evaluated using data quality control tools built into the new software. At the completion of the evaluation, the new datasets are to be published as an update to the WOUDC and NDACC archives, and the entire dataset is to be made available to the scientific community. The procedure for reprocessing Dobson data and the results of the reanalysis on the archived record are presented in this paper. A summary of historical changes to 14 station records is also provided.

Retrospective Identification of the Virtual Analogues of Ballistic Missiles

Разработка ракет как сложных технических изделий требует системного анализа проектных параметров, тактических, эксплуатационных и технических характеристик реальных ракет, прошедших все этапы жизненного цикла: разработка, производство, испытания, эксплуатация. Одним из эффективных методов решения таких задач является анализ конструкторской и технологической документации, а также конкретных образцов изделий. Если по объективным причинам это невыполнимо, необходимо воссоздать виртуальные аналоги ранее созданных ракет. Наиболее подходящим для этих целей является метод ретроспективной идентификации, заключающийся в имитационном моделировании исследуемых ракет с последовательной детализацией их параметров. Рассмотрен процесс идентификации виртуальных аналогов одноступенчатых баллистических ракет - от анализа «уравнения существования» до имитационного моделирования траектории полета управляемой одноступенчатой баллистической ракеты с вертикальным стартом и программным изменением угла тангажа и угла атаки. Приведены параметры и характеристики виртуальных аналогов одноступенчатых баллистических ракет: 11 ракет с двигателями на жидких криогенных топливах; 13 ракет с двигателями на жидких высококипящих топливах; 12 ракет с двигателями на твердых топливах. Для ракет с ЖРД методом наименьших квадратов получены полиномы, определяющие зависимости от максимальной дальности полета, минимальных и средних значений относительных масс топлива и конструкции двигательной установки.