ارائه الگوریتمی جهت اندازه‌گیری میزان توجه کاربران برنامه‌های موبایل با استفاده از نظرات مربوط به برنامه‌ها
محورهای موضوعی : سامانه های پردازشی و ارتباطی چند رسانه ای هوشمند
مهرداد رضوی دهکردی
1
(گروه مهندسی کامپیوتر، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران)
حمید رستگاری
2
(استادیار، گروه مهندسی کامپیوتر، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف آباد، ایران)
اکبر نبی اللهی نجف آبادی
3
(استادیار، گروه مهندسی کامپیوتر، واحد نجف‌آباد، دانشگاه آزاد اسلامی، نجف‌آباد، ایران)
تقی جاودانی گندمانی
4
(استادیار، گروه علوم کامپیوتر، دانشگاه شهرکرد، شهرکرد، ایران)
کلید واژه: برنامه‌های موبایل, میزان توجه, آنالیز فروشگاه گوگل, میزان توجه کاربران,
چکیده مقاله :
از زمان پیدایش برنامه‌های موبایل، نظرات کاربران برای توسعه‌دهندگان برنامه بسیار ارزشمند بوده است. به دلیل حجم بالای نظرات، تحلیل آن‌ها کار بسیار مشکلی است. تاکنون کارهای بسیاری درزمینه‌ی دسته‌بندی نظرات انجام شده است؛ اما در هیچ‌کدام از آن‌ها مشخص نشده است که پس از دسته‌بندی، توجه کاربران بیشتر به کدام دسته بوده است. در این مقاله الگوریتمی بانام UAR بر اساس تاریخ ثبت نظرات و تعداد آن‌ها برای اندازه‌گیری میزان توجه کاربران به دسته گزارش مشکل یا درخواست ویژگی ارائه شده است. برای آزمایش الگوریتم مورد نظر، نظرات برنامه‌های WhatsApp، NextCloud، Firefox Mozillaو VLC Media Player توسط خزنده وب به مدت 30 روز استخراج شده و سپس نظرات پس از دسته‌بندی به 4 متخصص نرم‌افزار داده شد. در ادامه پس از تحلیل نظرات توسط 4 متخصص میزان توجه کاربران به نظرات توسط آن‌ها مورد محاسبه قرار گرفت. سپس با استفاده از روش پیشنهادی نیز میزان توجه کاربران به نظرات اندازه‌گیری شد. در نهایت مشخص شد که روش پیشنهادی توانسته نتایج قابل قبولی در محاسبه میزان توجه کاربران به نظرات نسبت به مقادیر محاسبه شده توسط متخصصان داشته باشد؛ به‌طوری که تفاوت میزان توجه محاسبه شده توسط متخصصان با میزان توجه محاسبه شده توسط الگوریتم ارائه شده 2.73% بوده است
Introduction: Since the emergence of mobile apps, user reviews have been of great importance for app developers, as they contain users’ sentiment, bugs and new requests, user feedback has been valuable to app developers. Due to the large number of reviews, analysing them is a difficult, time-consuming and error-prone task. So far, many works have been done in the field of classifying reviews; But in none of them, it is not clear which category the users paid more attention to after categorization.
Method: In this article, an algorithm called UAR(User Attention Rate) is presented based on the date of reviews and their number to measure the level of attention of users to the category of bug report or feature request. To test the proposed algorithm, the reviews of WhatsApp, NextCloud, Mozilla Firefox and VLC Media Player apps were extracted by the web crawler for 30 days, and then the reviews were given to 4 software experts after classification. Next, after analyzing the reviews by 4 experts, the level of attention of users to the reviews is calculated by them. Then, using the proposed method, amount of users' attention to reviews is measured.
Results: It was found that the proposed algorithm able to have acceptable results in calculating the amount of users' attention to reviews compared to the values calculated by experts. So that the difference in the attention rate calculated by experts according to the proposed algorithm was 2.73%.
Discussion: Due to the large number of reviews posted for apps, it is difficult and time-consuming to review them by the development team. Having a method for User Attention Rate could save the development team’s time and help implement new features in the app, fix bugs and make the app successful. So far, many methods have been presented for automated, however, most of them have focused on invalid old features or ignored new features. A new algorithm is presented to calculate the users' attention rate to the categories of feature requests and bug reports. By using the relevant algorithm, the development team will understand that the user wants to fix the bugs related to the application or add new features to the application.
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